docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links

- ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline - README: Add dual-modal pose fusion demo link, update ADR count to 49 - References issue #245 Co-Authored-By: claude-flow <ruv@ruv.net>
feat: live ESP32 CSI pipeline + auto-connect WebSocket
2026-06-12 10:43:19 +00:00 · 2026-03-12 17:40:16 -04:00 · 2026-03-12 17:37:27 -04:00 · 2026-03-12 16:16:07 -04:00 · 2026-03-12 16:10:29 -04:00 · 2026-03-12 14:26:39 -04:00
41 changed files with 3948 additions and 4030 deletions
@@ -75,7 +75,7 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
 |----------|-------------|
 | [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
 | [Build Guide](docs/build-guide.md) | Building from source (Rust and Python) |
-| [Architecture Decisions](docs/adr/README.md) | 48 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) |
+| [Architecture Decisions](docs/adr/README.md) | 49 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure) |
 | [Domain Models](docs/ddd/README.md) | 7 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI) — bounded contexts, aggregates, domain events, and ubiquitous language |
 | [Desktop App](rust-port/wifi-densepose-rs/crates/wifi-densepose-desktop/README.md) | **WIP** — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization |

@@ -89,8 +89,12 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
  <em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em>
  <br>
  <a href="https://ruvnet.github.io/RuView/"><strong>▶ Live Observatory Demo</strong></a>
+  &nbsp;|&nbsp;
+  <a href="https://ruvnet.github.io/RuView/pose-fusion.html"><strong>▶ Dual-Modal Pose Fusion Demo</strong></a>

 > The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.
+>
+> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).


 ## 🚀 Key Features
@@ -1043,14 +1047,16 @@ Download a pre-built binary — no build toolchain needed:

 | Release | What's included | Tag |
 |---------|-----------------|-----|
-| [v0.2.0](https://github.com/ruvnet/RuView/releases/tag/v0.2.0-esp32) | Stable — raw CSI streaming, multi-node TDM, channel hopping | `v0.2.0-esp32` |
+| [v0.4.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.1-esp32) | **Stable** — CSI build fix, compile guard, AMOLED display, edge intelligence ([ADR-057](docs/adr/ADR-057-firmware-csi-build-guard.md)) | `v0.4.1-esp32` |
 | [v0.3.0-alpha](https://github.com/ruvnet/RuView/releases/tag/v0.3.0-alpha-esp32) | Alpha — adds on-device edge intelligence and WASM modules ([ADR-039](docs/adr/ADR-039-esp32-edge-intelligence.md), [ADR-040](docs/adr/ADR-040-wasm-programmable-sensing.md)) | `v0.3.0-alpha-esp32` |
+| [v0.2.0](https://github.com/ruvnet/RuView/releases/tag/v0.2.0-esp32) | Raw CSI streaming, multi-node TDM, channel hopping | `v0.2.0-esp32` |

 ```bash
 # 1. Flash the firmware to your ESP32-S3
 python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
-  write_flash --flash_mode dio --flash_size 8MB \
-  0x0 bootloader.bin 0x8000 partition-table.bin 0x10000 esp32-csi-node.bin
+  write_flash --flash-mode dio --flash-size 8MB --flash-freq 80m \
+  0x0 bootloader.bin 0x8000 partition-table.bin \
+  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin

 # 2. Set WiFi credentials and server address (stored in flash, survives reboots)
 python firmware/esp32-csi-node/provision.py --port COM7 \
@@ -1,240 +0,0 @@
-# ADR-057: Desktop App Training & RuVector Integration
-
-| Field | Value |
-|-------|-------|
-| Status | Proposed |
-| Date | 2026-03-10 |
-| Authors | RuView Team |
-| Reviewers | - |
-| Related | ADR-016, ADR-017, ADR-024, ADR-027 |
-
-## Context
-
-The RuView desktop application currently provides device discovery, firmware flashing, OTA updates, and real-time sensing visualization. However, users cannot train models or configure RuVector signal processing modules directly from the desktop app.
-
-The following crates exist in the workspace but are not exposed in the desktop UI:
-
-### Training Crate (`wifi-densepose-train`)
- Dataset management (MM-Fi, Wi-Pose formats)
- Model architectures (CSI encoder, pose decoder)
- Training loops with metrics tracking
- Checkpoint save/load
- ruview_metrics integration
-
-### RuVector Crates (5 modules)
-1. **ruvector-mincut** - Graph-based person segmentation, DynamicPersonMatcher
-2. **ruvector-attn-mincut** - Attention-weighted antenna selection
-3. **ruvector-temporal-tensor** - Temporal CSI compression, breathing detection
-4. **ruvector-solver** - Sparse interpolation, triangulation
-5. **ruvector-attention** - Spatial attention, BVP extraction
-
-## Decision
-
-Add a new **"Training"** page to the desktop application with tabbed navigation:
-
-### Tab Structure
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│  Training & Models                                          │
-├──────────┬──────────┬──────────┬──────────┬────────────────┤
-│ Datasets │  Models  │ Training │ RuVector │    Metrics     │
-└──────────┴──────────┴──────────┴──────────┴────────────────┘
-```
-
-### Tab 1: Datasets
- **Download** standard datasets (MM-Fi, Wi-Pose)
- **Import** custom CSI recordings
- **Preview** dataset samples (CSI heatmaps, labels)
- **Split** into train/val/test sets
- **Statistics** - sample counts, class distribution
-
-### Tab 2: Models
- **Browse** available architectures:
-  - CSI Encoder (CNN, Transformer)
-  - Pose Decoder (LSTM, GRU)
-  - AETHER embedding network (ADR-024)
-  - MERIDIAN domain adaptor (ADR-027)
- **Load** checkpoints from disk
- **View** model summary (params, layers, memory)
- **Export** to ONNX/TorchScript
-
-### Tab 3: Training
- **Configure** training:
-  - Learning rate, batch size, epochs
-  - Optimizer (Adam, SGD, AdamW)
-  - Loss function selection
-  - Data augmentation toggles
- **GPU Detection** - CUDA/Metal availability
- **Start/Stop** training jobs
- **Progress** - live loss curves, ETA
- **Checkpointing** - auto-save best model
-
-### Tab 4: RuVector
- **Module Configuration**:
-  - MinCut graph parameters
-  - Attention weights
-  - Temporal compression ratio
-  - Solver interpolation settings
- **Live Testing** - apply to real-time CSI stream
- **Comparison** - A/B test configurations
- **Export** - save optimal config
-
-### Tab 5: Metrics
- **Loss Curves** - training/validation over epochs
- **Evaluation** - PCK, mAP, IoU scores
- **Confusion Matrix** - per-joint accuracy
- **Export** - CSV, JSON, TensorBoard format
-
-## Architecture
-
-### Backend (Rust/Tauri)
-
-```
-wifi-densepose-desktop/
-├── src/
-│   ├── commands/
-│   │   ├── training.rs      # NEW: Training job management
-│   │   ├── datasets.rs      # NEW: Dataset download/import
-│   │   ├── models.rs        # NEW: Model loading/export
-│   │   ├── ruvector.rs      # NEW: RuVector config
-│   │   └── metrics.rs       # NEW: Metrics retrieval
-│   └── domain/
-│       ├── training.rs      # Training state machine
-│       └── ruvector.rs      # RuVector config types
-```
-
-### Frontend (React/TypeScript)
-
-```
-ui/src/pages/
-├── Training/
-│   ├── index.tsx            # Tab container
-│   ├── DatasetsTab.tsx      # Dataset management
-│   ├── ModelsTab.tsx        # Model browser
-│   ├── TrainingTab.tsx      # Training control
-│   ├── RuVectorTab.tsx      # Signal processing config
-│   └── MetricsTab.tsx       # Visualization
-```
-
-### Tauri Commands
-
-| Command | Description |
-|---------|-------------|
-| `list_datasets` | Get available datasets |
-| `download_dataset` | Download standard dataset |
-| `import_dataset` | Import custom recordings |
-| `list_models` | Get model architectures |
-| `load_checkpoint` | Load model weights |
-| `export_model` | Export to ONNX |
-| `detect_gpu` | Check CUDA/Metal |
-| `start_training` | Begin training job |
-| `stop_training` | Cancel training |
-| `training_progress` | Get current status |
-| `get_ruvector_config` | Load RuVector settings |
-| `set_ruvector_config` | Update settings |
-| `test_ruvector_live` | Apply to live CSI |
-| `get_metrics` | Retrieve training metrics |
-
-### Event System
-
-Training progress updates via Tauri events:
-
-```rust
-#[derive(Serialize, Clone)]
-pub struct TrainingProgress {
-    pub epoch: u32,
-    pub total_epochs: u32,
-    pub batch: u32,
-    pub total_batches: u32,
-    pub train_loss: f32,
-    pub val_loss: Option<f32>,
-    pub learning_rate: f32,
-    pub eta_secs: u64,
-    pub gpu_memory_mb: Option<u64>,
-}
-
-// Emit every batch
-app.emit("training:progress", progress)?;
-
-// Emit on completion
-app.emit("training:complete", result)?;
-```
-
-## Implementation Plan
-
-### Phase 1: Foundation (Week 1-2)
-1. Create `Training` page skeleton with tabs
-2. Implement `detect_gpu` command
-3. Add dataset listing/download commands
-4. Design TypeScript types for all entities
-
-### Phase 2: Dataset Management (Week 3)
-1. MM-Fi dataset downloader
-2. Wi-Pose dataset downloader
-3. Custom dataset import (CSV/NPZ)
-4. Dataset preview component
-
-### Phase 3: Model Management (Week 4)
-1. Model architecture browser
-2. Checkpoint loading
-3. Model summary display
-4. ONNX export
-
-### Phase 4: Training Loop (Week 5-6)
-1. Training configuration UI
-2. Background training thread
-3. Progress event emission
-4. Checkpoint auto-save
-5. Training history persistence
-
-### Phase 5: RuVector Integration (Week 7)
-1. RuVector config UI
-2. Live CSI testing
-3. A/B comparison mode
-4. Config export/import
-
-### Phase 6: Metrics & Polish (Week 8)
-1. Loss curve visualization (Chart.js/Recharts)
-2. Evaluation metrics display
-3. Export functionality
-4. Error handling & edge cases
-
-## Risks & Mitigations
-
-| Risk | Probability | Impact | Mitigation |
-|------|-------------|--------|------------|
-| No GPU available | Medium | High | CPU fallback with warning |
-| Large dataset downloads | High | Medium | Resume support, progress UI |
-| Training crashes | Medium | High | Checkpoint recovery, error reporting |
-| Memory exhaustion | Low | High | Batch size auto-tuning |
-| UI blocking | Medium | High | All training in background thread |
-
-## Success Criteria
-
-1. User can download MM-Fi dataset from UI
-2. User can start training with GPU detection
-3. Live progress updates without UI freeze
-4. Training can be paused/resumed
-5. RuVector config changes apply to live CSI
-6. Metrics display updates in real-time
-7. Models can be exported to ONNX
-
-## Alternatives Considered
-
-### 1. Separate Training App
- **Rejected**: Fragments user experience, duplicates code
-
-### 2. Web-based Training Dashboard
- **Rejected**: Requires server, no offline support
-
-### 3. CLI-only Training
- **Rejected**: Poor UX for non-technical users
-
-## References
-
- ADR-016: RuVector Training Pipeline Integration
- ADR-017: RuVector Signal + MAT Integration
- ADR-024: AETHER Contrastive CSI Embedding
- ADR-027: MERIDIAN Domain Generalization
- Tauri v2 Events: https://v2.tauri.app/develop/calling-rust/#events
@@ -0,0 +1,82 @@
+# ADR-057: Firmware CSI Build Guard and sdkconfig.defaults
+
+| Field       | Value                                       |
+|-------------|---------------------------------------------|
+| **Status**  | Accepted                                    |
+| **Date**    | 2026-03-12                                  |
+| **Authors** | ruv                                         |
+| **Issues**  | #223, #238, #234, #210, #190                |
+
+## Context
+
+Multiple GitHub issues (#223, #238, #234, #210, #190) report firmware problems
+that fall into two categories:
+
+1. **CSI not enabled at runtime** — The committed `sdkconfig` had
+   `# CONFIG_ESP_WIFI_CSI_ENABLED is not set` (line 1135), meaning users who
+   built from source or used pre-built binaries got the runtime error:
+   `E (6700) wifi:CSI not enabled in menuconfig!`
+
+   Root cause: `sdkconfig.defaults.template` existed with the correct setting
+   (`CONFIG_ESP_WIFI_CSI_ENABLED=y`) but ESP-IDF only reads
+   `sdkconfig.defaults` — not `.template` suffixed files. No `sdkconfig.defaults`
+   file was committed.
+
+2. **Unsupported ESP32 variants** — Users attempting to use original ESP32
+   (D0WD) and ESP32-C3 boards. The firmware targets ESP32-S3 only
+   (`CONFIG_IDF_TARGET="esp32s3"`, Xtensa architecture) and this was not
+   surfaced clearly enough in documentation or build errors.
+
+## Decision
+
+### Fix 1: Commit `sdkconfig.defaults` (not just template)
+
+Copy `sdkconfig.defaults.template` → `sdkconfig.defaults` so that ESP-IDF
+applies the correct defaults (including `CONFIG_ESP_WIFI_CSI_ENABLED=y`)
+automatically when `sdkconfig` is regenerated.
+
+### Fix 2: `#error` compile-time guard in `csi_collector.c`
+
+Add a preprocessor guard:
+
+```c
+#ifndef CONFIG_ESP_WIFI_CSI_ENABLED
+#error "CONFIG_ESP_WIFI_CSI_ENABLED must be set in sdkconfig."
+#endif
+```
+
+This turns a confusing runtime crash into a clear compile-time error with
+instructions on how to fix it.
+
+### Fix 3: Fix committed `sdkconfig`
+
+Change line 1135 from `# CONFIG_ESP_WIFI_CSI_ENABLED is not set` to
+`CONFIG_ESP_WIFI_CSI_ENABLED=y`.
+
+## Consequences
+
+- **Positive**: New builds will always have CSI enabled. Users building from
+  source will get a clear compile error if CSI is somehow disabled.
+- **Positive**: Pre-built release binaries will include CSI support.
+- **Neutral**: Original ESP32 and ESP32-C3 remain unsupported. This is by
+  design — only ESP32-S3 has the CSI API surface we depend on. Future ADRs
+  may address multi-target support if demand warrants it.
+- **Negative**: None identified.
+
+## Hardware Support Matrix
+
+| Variant      | CSI Support | Firmware Target | Status        |
+|--------------|-------------|-----------------|---------------|
+| ESP32-S3     | Yes         | Yes             | Supported     |
+| ESP32 (orig) | Partial     | No              | Unsupported   |
+| ESP32-C3     | Yes (IDF 5.1+) | No           | Unsupported   |
+| ESP32-C6     | Yes         | No              | Unsupported   |
+
+## Notes
+
+- ESP32-C3 and C6 use RISC-V architecture; a separate build target
+  (`idf.py set-target esp32c3`) would be needed.
+- Original ESP32 has limited CSI (no STBC HT-LTF2, fewer subcarriers).
+- Users on unsupported hardware can still write custom firmware using the
+  ADR-018 binary frame format (magic `0xC5110001`) for interop with the
+  Rust aggregator.
@@ -0,0 +1,392 @@
+# ADR-058: Dual-Modal WASM Browser Pose Estimation — Live Video + WiFi CSI Fusion
+
+- **Status**: Proposed
+- **Date**: 2026-03-12
+- **Deciders**: ruv
+- **Tags**: wasm, browser, cnn, pose-estimation, ruvector, video, multimodal, fusion
+
+## Context
+
+WiFi-DensePose estimates human poses from WiFi CSI (Channel State Information).
+The `ruvector-cnn` crate provides a pure Rust CNN (MobileNet-V3) with WASM bindings.
+Both modalities exist independently — what's missing is **fusing live webcam video
+with WiFi CSI** in a single browser demo to achieve robust pose estimation that
+works even when one modality degrades (occlusion, signal noise, poor lighting).
+
+Existing assets:
+
+1. **`wifi-densepose-wasm`** — CSI signal processing compiled to WASM
+2. **`wifi-densepose-sensing-server`** — Axum server streaming live CSI via WebSocket
+3. **`ruvector-cnn`** — Pure Rust CNN with MobileNet-V3 backbones, SIMD, contrastive learning
+4. **`ruvector-cnn-wasm`** — wasm-bindgen bindings: `WasmCnnEmbedder`, `SimdOps`, `LayerOps`, contrastive losses
+5. **`vendor/ruvector/examples/wasm-vanilla/`** — Reference vanilla JS WASM example
+
+Research shows multi-modal fusion (camera + WiFi) significantly outperforms either alone:
+- Camera fails under occlusion, poor lighting, privacy constraints
+- WiFi CSI fails with signal noise, multipath, low spatial resolution
+- Fusion compensates: WiFi provides through-wall coverage, camera provides fine-grained detail
+
+## Decision
+
+Build a **dual-modal browser demo** at `examples/wasm-browser-pose/` that:
+
+1. Captures **live webcam video** via `getUserMedia` API
+2. Receives **live WiFi CSI** via WebSocket from the sensing server
+3. Processes **both streams** through separate CNN pipelines in `ruvector-cnn-wasm`
+4. **Fuses embeddings** with learned attention weights for combined pose estimation
+5. Renders **video overlay** with skeleton + WiFi confidence heatmap on Canvas
+6. Runs entirely in the browser — all inference client-side via WASM
+
+### Architecture
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│  Browser                                                         │
+│                                                                  │
+│  ┌────────────┐    ┌────────────────┐    ┌───────────────────┐   │
+│  │ getUserMedia│───▶│ Video Frame    │───▶│ CNN WASM          │   │
+│  │ (Webcam)   │    │ Capture        │    │ (Visual Embedder) │   │
+│  └────────────┘    │ 224×224 RGB    │    │ → 512-dim         │   │
+│                    └────────────────┘    └────────┬──────────┘   │
+│                                                   │              │
+│                                          visual_embedding        │
+│                                                   │              │
+│                                            ┌──────▼──────┐       │
+│  ┌────────────┐    ┌────────────────┐      │             │       │
+│  │ WebSocket  │───▶│ CSI WASM       │      │  Attention  │       │
+│  │ Client     │    │ (densepose-    │      │  Fusion     │       │
+│  │            │    │  wasm)         │      │  Module     │       │
+│  └────────────┘    └───────┬────────┘      │             │       │
+│                            │               └──────┬──────┘       │
+│                    ┌───────▼────────┐             │              │
+│                    │ CNN WASM       │      fused_embedding       │
+│                    │ (CSI Embedder) │             │              │
+│                    │ → 512-dim      │      ┌──────▼──────┐       │
+│                    └───────┬────────┘      │ Pose        │       │
+│                            │               │ Decoder     │       │
+│                     csi_embedding           │ → 17 kpts   │       │
+│                            │               └──────┬──────┘       │
+│                            └──────────────────────┘              │
+│                                                   │              │
+│                    ┌──────────────┐         ┌─────▼──────┐       │
+│                    │ Video Canvas │◀────────│ Overlay    │       │
+│                    │ + Skeleton   │         │ Renderer   │       │
+│                    │ + Heatmap    │         └────────────┘       │
+│                    └──────────────┘                               │
+│                                                                  │
+└──────────────────────────────────────────────────────────────────┘
+         ▲                                     ▲
+         │ getUserMedia                        │ WebSocket
+         │ (camera)                            │ (ws://host:3030/ws/csi)
+         │                                     │
+    ┌────┴────┐                        ┌───────┴─────────┐
+    │ Webcam  │                        │ Sensing Server   │
+    └─────────┘                        └─────────────────┘
+```
+
+### Dual Pipeline Design
+
+Two parallel CNN pipelines run on each frame tick (~30 FPS):
+
+| Pipeline | Input | Preprocessing | CNN Config | Output |
+|----------|-------|---------------|------------|--------|
+| **Visual** | Webcam frame (640×480) | Resize to 224×224 RGB, ImageNet normalize | MobileNet-V3 Small, 512-dim | Visual embedding |
+| **CSI** | CSI frame (ADR-018 binary) | Amplitude/phase/delta → 224×224 pseudo-RGB | MobileNet-V3 Small, 512-dim | CSI embedding |
+
+Both use the same `WasmCnnEmbedder` but with separate instances and weight sets.
+
+### Fusion Strategy
+
+**Learned attention-weighted fusion** combines the two 512-dim embeddings:
+
+```javascript
+// Attention fusion: learn which modality to trust per-dimension
+// α ∈ [0,1]^512 — attention weights (shipped as JSON, trained offline)
+// visual_emb, csi_emb ∈ R^512
+
+function fuseEmbeddings(visual_emb, csi_emb, attention_weights) {
+    const fused = new Float32Array(512);
+    for (let i = 0; i < 512; i++) {
+        const α = attention_weights[i];
+        fused[i] = α * visual_emb[i] + (1 - α) * csi_emb[i];
+    }
+    return fused;
+}
+```
+
+**Dynamic confidence gating** adjusts fusion based on signal quality:
+
+| Condition | Behavior |
+|-----------|----------|
+| Good video + good CSI | Balanced fusion (α ≈ 0.5) |
+| Poor lighting / occlusion | CSI-dominant (α → 0, WiFi takes over) |
+| CSI noise / no ESP32 | Video-dominant (α → 1, camera only) |
+| Video-only mode (no WiFi) | α = 1.0, pure visual CNN pose estimation |
+| CSI-only mode (no camera) | α = 0.0, pure WiFi pose estimation |
+
+Quality detection:
+- **Video quality**: Frame brightness variance (dark = low quality), motion blur score
+- **CSI quality**: Signal-to-noise ratio from `wifi-densepose-wasm`, coherence gate output
+
+### CSI-to-Image Encoding
+
+CSI data encoded as 3-channel pseudo-image for the CSI CNN pipeline:
+
+| Channel | Data | Normalization |
+|---------|------|---------------|
+| R | CSI amplitude (subcarrier × time window) | Min-max to [0, 255] |
+| G | CSI phase (unwrapped, subcarrier × time window) | Min-max to [0, 255] |
+| B | Temporal difference (frame-to-frame Δ amplitude) | Abs, min-max to [0, 255] |
+
+### Video Processing
+
+Webcam frames processed through standard ImageNet pipeline:
+
+```javascript
+// Capture frame from video element
+const frame = captureVideoFrame(videoElement, 224, 224); // Returns Uint8Array RGB
+
+// ImageNet normalization happens inside WasmCnnEmbedder.extract()
+const visual_embedding = visual_embedder.extract(frame, 224, 224);
+```
+
+### Pose Keypoint Mapping
+
+17 COCO-format keypoints decoded from the fused 512-dim embedding:
+
+```
+ 0: nose          1: left_eye       2: right_eye
+ 3: left_ear      4: right_ear      5: left_shoulder
+ 6: right_shoulder 7: left_elbow    8: right_elbow
+ 9: left_wrist   10: right_wrist   11: left_hip
+12: right_hip    13: left_knee     14: right_knee
+15: left_ankle   16: right_ankle
+```
+
+Each keypoint decoded as (x, y, confidence) = 51 values from the 512-dim embedding
+via a learned linear projection.
+
+### Operating Modes
+
+The demo supports three modes, selectable in the UI:
+
+| Mode | Video | CSI | Fusion | Use Case |
+|------|-------|-----|--------|----------|
+| **Dual (default)** | ✅ | ✅ | Attention-weighted | Best accuracy, full demo |
+| **Video Only** | ✅ | ❌ | α = 1.0 | No ESP32 available, quick demo |
+| **CSI Only** | ❌ | ✅ | α = 0.0 | Privacy mode, through-wall sensing |
+
+**Video Only mode works without any hardware** — just a webcam — making the demo
+instantly accessible for anyone wanting to try it.
+
+### File Layout
+
+```
+examples/wasm-browser-pose/
+├── index.html                  # Single-page app (vanilla JS, no bundler)
+├── js/
+│   ├── app.js                  # Main entry, mode selection, orchestration
+│   ├── video-capture.js        # getUserMedia, frame extraction, quality detection
+│   ├── csi-processor.js        # WebSocket CSI client, frame parsing, pseudo-image encoding
+│   ├── fusion.js               # Attention-weighted embedding fusion, confidence gating
+│   ├── pose-decoder.js         # Fused embedding → 17 keypoints
+│   └── canvas-renderer.js      # Video overlay, skeleton, CSI heatmap, confidence bars
+├── data/
+│   ├── visual-weights.json     # Visual CNN → embedding projection (placeholder until trained)
+│   ├── csi-weights.json        # CSI CNN → embedding projection (placeholder until trained)
+│   ├── fusion-weights.json     # Attention fusion α weights (512 values)
+│   └── pose-weights.json       # Fused embedding → keypoint projection
+├── css/
+│   └── style.css               # Dark theme UI styling
+├── pkg/                        # Built WASM packages (gitignored, built by script)
+│   ├── wifi_densepose_wasm/
+│   └── ruvector_cnn_wasm/
+├── build.sh                    # wasm-pack build script for both packages
+└── README.md                   # Setup and usage instructions
+```
+
+### Build Pipeline
+
+```bash
+#!/bin/bash
+# build.sh — builds both WASM packages into pkg/
+
+set -e
+
+# Build wifi-densepose-wasm (CSI processing)
+wasm-pack build ../../rust-port/wifi-densepose-rs/crates/wifi-densepose-wasm \
+  --target web --out-dir "$(pwd)/pkg/wifi_densepose_wasm" --no-typescript
+
+# Build ruvector-cnn-wasm (CNN inference for both video and CSI)
+wasm-pack build ../../vendor/ruvector/crates/ruvector-cnn-wasm \
+  --target web --out-dir "$(pwd)/pkg/ruvector_cnn_wasm" --no-typescript
+
+echo "Build complete. Serve with: python3 -m http.server 8080"
+```
+
+### UI Layout
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  WiFi-DensePose — Live Dual-Modal Pose Estimation       │
+│  [Dual Mode ▼]  [⚙ Settings]          FPS: 28  ◉ Live  │
+├───────────────────────────┬─────────────────────────────┤
+│                           │                             │
+│   ┌───────────────────┐   │   ┌───────────────────┐     │
+│   │                   │   │   │                   │     │
+│   │  Video + Skeleton │   │   │  CSI Heatmap      │     │
+│   │  Overlay          │   │   │  (amplitude ×     │     │
+│   │  (main canvas)    │   │   │   subcarrier)     │     │
+│   │                   │   │   │                   │     │
+│   └───────────────────┘   │   └───────────────────┘     │
+│                           │                             │
+├───────────────────────────┴─────────────────────────────┤
+│  Fusion Confidence: ████████░░ 78%                      │
+│  Video: ██████████ 95%  │  CSI: ██████░░░░ 61%          │
+├─────────────────────────────────────────────────────────┤
+│  ┌─────────────────────────────────────────────────┐    │
+│  │  Embedding Space (2D projection)                 │    │
+│  │     ·  ·    ·                                    │    │
+│  │   · · ·  ·    · ·    (color = pose cluster)     │    │
+│  │      ·  · · ·                                    │    │
+│  └─────────────────────────────────────────────────┘    │
+├─────────────────────────────────────────────────────────┤
+│  Latency: Video 12ms │ CSI 8ms │ Fusion 1ms │ Total 21ms│
+│  [▶ Record]  [📷 Snapshot]  [Confidence: ████ 0.6]      │
+└─────────────────────────────────────────────────────────┘
+```
+
+### WASM Module Structure
+
+| Package | Source Crate | Provides | Size (est.) |
+|---------|-------------|----------|-------------|
+| `wifi_densepose_wasm` | `wifi-densepose-wasm` | CSI frame parsing, signal processing, feature extraction | ~200KB |
+| `ruvector_cnn_wasm` | `ruvector-cnn-wasm` | `WasmCnnEmbedder` (×2 instances), `SimdOps`, `LayerOps`, contrastive losses | ~150KB |
+
+Two `WasmCnnEmbedder` instances are created — one for video frames, one for CSI pseudo-images.
+They share the same WASM module but have independent state.
+
+### Browser API Requirements
+
+| API | Purpose | Required | Fallback |
+|-----|---------|----------|----------|
+| `getUserMedia` | Webcam capture | For video mode | CSI-only mode |
+| WebAssembly | CNN inference | Yes | None (hard requirement) |
+| WASM SIMD128 | Accelerated inference | No | Scalar fallback (~2× slower) |
+| WebSocket | CSI data stream | For CSI mode | Video-only mode |
+| Canvas 2D | Rendering | Yes | None |
+| `requestAnimationFrame` | Render loop | Yes | `setTimeout` fallback |
+| ES Modules | Code organization | Yes | None |
+
+Target: Chrome 89+, Firefox 89+, Safari 15+, Edge 89+
+
+### Performance Budget
+
+| Stage | Target Latency | Notes |
+|-------|---------------|-------|
+| Video frame capture + resize | <3ms | `drawImage` to offscreen canvas |
+| Video CNN embedding | <15ms | 224×224 RGB → 512-dim |
+| CSI receive + parse | <2ms | Binary WebSocket message |
+| CSI pseudo-image encoding | <3ms | Amplitude/phase/delta channels |
+| CSI CNN embedding | <15ms | 224×224 pseudo-RGB → 512-dim |
+| Attention fusion | <1ms | Element-wise weighted sum |
+| Pose decoding | <1ms | Linear projection |
+| Canvas overlay render | <3ms | Video + skeleton + heatmap |
+| **Total (dual mode)** | **<33ms** | **30 FPS capable** |
+| **Total (video only)** | **<22ms** | **45 FPS capable** |
+
+Note: Video and CSI CNN pipelines can run in parallel using Web Workers,
+reducing dual-mode latency to ~max(15, 15) + 5 = ~20ms (50 FPS).
+
+### Contrastive Learning Integration
+
+The demo optionally shows real-time contrastive learning in the browser:
+
+- **InfoNCE loss** (`WasmInfoNCELoss`): Compare video vs CSI embeddings for the same pose — trains cross-modal alignment
+- **Triplet loss** (`WasmTripletLoss`): Push apart different poses, pull together same pose across modalities
+- **SimdOps**: Accelerated dot products for real-time similarity computation
+- **Embedding space panel**: Live 2D projection shows video and CSI embeddings converging when viewing the same person
+
+### Relationship to Existing Crates
+
+| Existing Crate | Role in This Demo |
+|---------------|-------------------|
+| `ruvector-cnn-wasm` | CNN inference for **both** video frames and CSI pseudo-images |
+| `wifi-densepose-wasm` | CSI frame parsing and signal processing |
+| `wifi-densepose-sensing-server` | WebSocket CSI data source |
+| `wifi-densepose-core` | ADR-018 frame format definitions |
+| `ruvector-cnn` | Underlying MobileNet-V3, layers, contrastive learning |
+
+No new Rust crates are needed. The example is pure HTML/JS consuming existing WASM packages.
+
+## Consequences
+
+### Positive
+
+- **Instant demo**: Video-only mode works with just a webcam — no ESP32 needed
+- **Multi-modal showcase**: Demonstrates camera + WiFi fusion, the core innovation of the project
+- **Graceful degradation**: Works with video-only, CSI-only, or both
+- **Through-wall capability**: CSI mode shows pose estimation where cameras cannot reach
+- **Zero-install**: Anyone with a browser can try it
+- **Training data collection**: Can record paired (video, CSI) data for offline model training
+- **Reusable**: JS modules embed directly in the Tauri desktop app's webview
+
+### Negative
+
+- **Model weights**: Requires offline-trained weights for visual CNN, CSI CNN, fusion, and pose decoder (~200KB total JSON)
+- **WASM size**: Two WASM modules total ~350KB (acceptable)
+- **No GPU**: CPU-only WASM inference; adequate at 224×224 but limits resolution scaling
+- **Camera privacy**: Video mode requires camera permission (mitigated: CSI-only mode available)
+- **Two CNN instances**: Memory footprint doubles vs single-modal (~10MB total, acceptable for desktop browsers)
+
+### Risks
+
+- **Cross-modal alignment**: Video and CSI embeddings must be trained jointly for fusion to work;
+  without proper training, fusion may be worse than either modality alone
+- **Latency on mobile**: Dual CNN on mobile browsers may exceed 33ms; implement automatic quality reduction
+- **WebSocket drops**: Network jitter → CSI frame gaps; buffer last 3 frames, interpolate missing data
+
+## Implementation Plan
+
+1. **Phase 1 — Scaffold**: File layout, build.sh, index.html shell, mode selector UI
+2. **Phase 2 — Video pipeline**: getUserMedia → frame capture → CNN embedding → basic pose display
+3. **Phase 3 — CSI pipeline**: WebSocket client → CSI parsing → pseudo-image → CNN embedding
+4. **Phase 4 — Fusion**: Attention-weighted combination, confidence gating, mode switching
+5. **Phase 5 — Pose decoder**: Linear projection with placeholder weights → 17 keypoints
+6. **Phase 6 — Overlay renderer**: Video canvas with skeleton overlay, CSI heatmap panel
+7. **Phase 7 — Training**: Use `wifi-densepose-train` to generate real weights for both CNNs + fusion + decoder
+8. **Phase 8 — Contrastive demo**: Embedding space visualization, cross-modal similarity display
+9. **Phase 9 — Web Workers**: Move CNN inference to workers for parallel video + CSI processing
+10. **Phase 10 — Polish**: Recording, snapshots, adaptive quality, mobile optimization
+
+## Alternatives Considered
+
+### 1. CSI-Only (No Video)
+Rejected: Misses the opportunity to show multi-modal fusion and makes the demo less
+accessible (requires ESP32 hardware). Video-only mode as a fallback is strictly better.
+
+### 2. Server-Side Video Inference
+Rejected: Adds latency, requires webcam stream upload (privacy concern), and defeats
+the WASM-first architecture. All inference must be client-side.
+
+### 3. TensorFlow.js for Video, ruvector-cnn-wasm for CSI
+Rejected: Would require two different ML frameworks. Using `ruvector-cnn-wasm` for both
+keeps a single WASM module, unified embedding space, and simpler fusion.
+
+### 4. Pre-recorded Video Demo
+Rejected: Live webcam input is far more compelling for demonstrations.
+Pre-recorded mode can be added as a secondary option.
+
+### 5. React/Vue Framework
+Rejected: Adds build tooling. Vanilla JS + ES modules keeps the demo self-contained.
+
+## References
+
+- [ADR-018: Binary CSI Frame Format](ADR-018-binary-csi-frame-format.md)
+- [ADR-024: Contrastive CSI Embedding / AETHER](ADR-024-contrastive-csi-embedding.md)
+- [ADR-055: Integrated Sensing Server](ADR-055-integrated-sensing-server.md)
+- `vendor/ruvector/crates/ruvector-cnn/src/lib.rs` — CNN embedder implementation
+- `vendor/ruvector/crates/ruvector-cnn-wasm/src/lib.rs` — WASM bindings
+- `vendor/ruvector/examples/wasm-vanilla/index.html` — Reference vanilla JS WASM pattern
+- Person-in-WiFi: Fine-grained Person Perception using WiFi (ICCV 2019) — camera+WiFi fusion precedent
+- WiPose: Multi-Person WiFi Pose Estimation (TMC 2022) — cross-modal embedding approach
@@ -0,0 +1,83 @@
+# ADR-059: Live ESP32 CSI Pipeline Integration
+
+## Status
+
+Accepted
+
+## Date
+
+2026-03-12
+
+## Context
+
+ADR-058 established a dual-modal browser demo combining webcam video and WiFi CSI for pose estimation. However, it used simulated CSI data. To demonstrate real-world capability, we need an end-to-end pipeline from physical ESP32 hardware through to the browser visualization.
+
+The ESP32-S3 firmware (`firmware/esp32-csi-node/`) already supports CSI collection and UDP streaming (ADR-018). The sensing server (`wifi-densepose-sensing-server`) already supports UDP ingestion and WebSocket bridging. The missing piece was connecting these components and enabling the browser demo to consume live data.
+
+## Decision
+
+Implement a complete live CSI pipeline:
+
+```
+ESP32-S3 (CSI capture) → UDP:5005 → sensing-server (Rust/Axum) → WS:8765 → browser demo
+```
+
+### Components
+
+1. **ESP32 Firmware** — Rebuilt with native Windows ESP-IDF v5.4.0 toolchain (no Docker). Configured for target network and PC IP via `sdkconfig`. Helper scripts added:
+   - `build_firmware.ps1` — Sets up IDF environment, cleans, builds, and flashes
+   - `read_serial.ps1` — Serial monitor with DTR/RTS reset capability
+
+2. **Sensing Server** — `wifi-densepose-sensing-server` started with:
+   - `--source esp32` — Expect real ESP32 UDP frames
+   - `--bind-addr 0.0.0.0` — Accept connections from any interface
+   - `--ui-path <path>` — Serve the demo UI via HTTP
+
+3. **Browser Demo** — `main.js` updated to auto-connect to `ws://localhost:8765/ws/sensing` on page load. Falls back to simulated CSI if the WebSocket is unavailable (GitHub Pages).
+
+### Network Configuration
+
+The ESP32 sends UDP packets to a configured target IP. If the PC's IP doesn't match the firmware's compiled target, a secondary IP alias can be added:
+
+```powershell
+# PowerShell (Admin)
+New-NetIPAddress -IPAddress 192.168.1.100 -PrefixLength 24 -InterfaceAlias "Wi-Fi"
+```
+
+### Data Flow
+
+| Stage | Protocol | Format | Rate |
+|-------|----------|--------|------|
+| ESP32 → Server | UDP | ADR-018 binary frame (magic `0xC5110001`, I/Q pairs) | ~100 Hz |
+| Server → Browser | WebSocket | ADR-018 binary frame (forwarded) | ~10 Hz (tick-ms=100) |
+| Browser decode | JavaScript | Float32 amplitude/phase arrays | Per frame |
+
+### Build Environment (Windows)
+
+ESP-IDF v5.4.0 on Windows requires:
+- IDF_PATH pointing to the ESP-IDF framework
+- IDF_TOOLS_PATH pointing to toolchain binaries
+- MSYS/MinGW environment variables removed (ESP-IDF rejects them)
+- Python venv from ESP-IDF tools for `idf.py` execution
+
+The `build_firmware.ps1` script handles all of this automatically.
+
+## Consequences
+
+### Positive
+- First end-to-end demonstration of real WiFi CSI → pose estimation in a browser
+- No Docker required for firmware builds on Windows
+- Demo gracefully degrades to simulated CSI when no server is available
+- Same demo works on GitHub Pages (simulated) and locally (live ESP32)
+
+### Negative
+- ESP32 target IP is compiled into firmware; changing it requires a rebuild or NVS override
+- Windows firewall may block UDP:5005; user must allow it
+- Mixed content restrictions prevent HTTPS pages from connecting to ws:// (local only)
+
+## Related
+
+- [ADR-018](ADR-018-esp32-dev-implementation.md) — ESP32 CSI frame format and UDP streaming
+- [ADR-058](ADR-058-ruvector-wasm-browser-pose-example.md) — Dual-modal WASM browser pose demo
+- [ADR-039](ADR-039-edge-intelligence-framework.md) — Edge intelligence on ESP32
+- Issue [#245](https://github.com/ruvnet/RuView/issues/245) — Tracking issue
@@ -78,6 +78,17 @@ docker pull ruvnet/wifi-densepose:latest

 Multi-architecture image (amd64 + arm64). Works on Intel/AMD and Apple Silicon Macs. Contains the Rust sensing server, Three.js UI, and all signal processing.

+**Data source selection:** Use the `CSI_SOURCE` environment variable to select the sensing mode:
+
+| Value | Description |
+|-------|-------------|
+| `auto` | (default) Probe for ESP32 on UDP 5005, fall back to simulation |
+| `esp32` | Receive real CSI frames from ESP32 devices over UDP |
+| `simulated` | Generate synthetic CSI frames (no hardware required) |
+| `wifi` | Host Wi-Fi RSSI (not available inside containers) |
+
+Example: `docker run -e CSI_SOURCE=esp32 -p 3000:3000 -p 5005:5005/udp ruvnet/wifi-densepose:latest`
+
 ### From Source (Rust)

 ```bash
@@ -267,8 +278,8 @@ Real Channel State Information at 20 Hz with 56-192 subcarriers. Required for po
 # From source
 ./target/release/sensing-server --source esp32 --udp-port 5005 --http-port 3000 --ws-port 3001

-# Docker
-docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest --source esp32
+# Docker (use CSI_SOURCE environment variable)
+docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp -e CSI_SOURCE=esp32 ruvnet/wifi-densepose:latest
 ```

 The ESP32 nodes stream binary CSI frames over UDP to port 5005. See [Hardware Setup](#esp32-s3-mesh) for flashing instructions.
@@ -679,9 +690,11 @@ Download the dataset files and place them in a `data/` directory.
 ./target/release/sensing-server --train --dataset data/ --dataset-type mmfi --epochs 100 --save-rvf model.rvf

 # Via Docker (mount your data directory)
+# Note: Training mode requires overriding the default entrypoint
 docker run --rm \
  -v $(pwd)/data:/data \
  -v $(pwd)/output:/output \
+  --entrypoint /app/sensing-server \
  ruvnet/wifi-densepose:latest \
  --train --dataset /data --epochs 100 --export-rvf /output/model.rvf
 ```
@@ -797,14 +810,18 @@ Pre-built binaries are available at [Releases](https://github.com/ruvnet/RuView/

 | Release | What It Includes | Tag |
 |---------|-----------------|-----|
-| [v0.2.0](https://github.com/ruvnet/RuView/releases/tag/v0.2.0-esp32) | Stable — raw CSI streaming, TDM, channel hopping, QUIC mesh | `v0.2.0-esp32` |
+| [v0.4.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.1-esp32) | **Stable** — CSI build fix, compile guard, AMOLED display, edge intelligence ([ADR-057](../docs/adr/ADR-057-firmware-csi-build-guard.md)) | `v0.4.1-esp32` |
 | [v0.3.0-alpha](https://github.com/ruvnet/RuView/releases/tag/v0.3.0-alpha-esp32) | Alpha — adds on-device edge intelligence (ADR-039) | `v0.3.0-alpha-esp32` |
+| [v0.2.0](https://github.com/ruvnet/RuView/releases/tag/v0.2.0-esp32) | Raw CSI streaming, TDM, channel hopping, QUIC mesh | `v0.2.0-esp32` |
+
+> **Important:** Firmware versions prior to v0.4.1 had CSI **disabled** in the build config, causing a runtime error (`E wifi:CSI not enabled in menuconfig!`). Always use v0.4.1 or later.

 ```bash
 # Flash an ESP32-S3 (requires esptool: pip install esptool)
 python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
-  write-flash --flash-mode dio --flash-size 4MB \
-  0x0 bootloader.bin 0x8000 partition-table.bin 0x10000 esp32-csi-node.bin
+  write-flash --flash-mode dio --flash-size 8MB --flash-freq 80m \
+  0x0 bootloader.bin 0x8000 partition-table.bin \
+  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin
 ```

 **Provisioning:**
@@ -885,8 +902,8 @@ Binary size: 777 KB (24% free in the 1 MB app partition).
 # From source
 ./target/release/sensing-server --source esp32 --udp-port 5005 --http-port 3000 --ws-port 3001

-# Docker
-docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp ruvnet/wifi-densepose:latest --source esp32
+# Docker (use CSI_SOURCE environment variable)
+docker run -p 3000:3000 -p 3001:3001 -p 5005:5005/udp -e CSI_SOURCE=esp32 ruvnet/wifi-densepose:latest
 ```

 See [ADR-018](../docs/adr/ADR-018-esp32-dev-implementation.md), [ADR-029](../docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md), and [Tutorial #34](https://github.com/ruvnet/RuView/issues/34).
@@ -953,12 +970,17 @@ Add the WebSocket port mapping:
 docker run -p 3000:3000 -p 3001:3001 ruvnet/wifi-densepose:latest
 ```

+### ESP32: "CSI not enabled in menuconfig"
+
+Firmware versions prior to v0.4.1 had `CONFIG_ESP_WIFI_CSI_ENABLED` disabled in the build config. Upgrade to [v0.4.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.1-esp32) or later. If building from source, ensure `sdkconfig.defaults` exists (not just `sdkconfig.defaults.template`). See [ADR-057](../docs/adr/ADR-057-firmware-csi-build-guard.md).
+
 ### ESP32: No data arriving

-1. Verify the ESP32 is connected to the same WiFi network
-2. Check the target IP matches the sensing server machine: `python firmware/esp32-csi-node/provision.py --port COM7 --target-ip <YOUR_IP>`
-3. Verify UDP port 5005 is not blocked by firewall
-4. Test with: `nc -lu 5005` (Linux) or similar UDP listener
+1. Verify firmware is v0.4.1+ (older versions had CSI disabled — see above)
+2. Verify the ESP32 is connected to the same WiFi network
+3. Check the target IP matches the sensing server machine: `python firmware/esp32-csi-node/provision.py --port COM7 --target-ip <YOUR_IP>`
+4. Verify UDP port 5005 is not blocked by firewall
+5. Test with: `nc -lu 5005` (Linux) or similar UDP listener

 ### Build: Rust compilation errors

@@ -0,0 +1,31 @@
+# Remove MSYS environment variables that trigger ESP-IDF's MinGW rejection
+Remove-Item env:MSYSTEM -ErrorAction SilentlyContinue
+Remove-Item env:MSYSTEM_CARCH -ErrorAction SilentlyContinue
+Remove-Item env:MSYSTEM_CHOST -ErrorAction SilentlyContinue
+Remove-Item env:MSYSTEM_PREFIX -ErrorAction SilentlyContinue
+Remove-Item env:MINGW_CHOST -ErrorAction SilentlyContinue
+Remove-Item env:MINGW_PACKAGE_PREFIX -ErrorAction SilentlyContinue
+Remove-Item env:MINGW_PREFIX -ErrorAction SilentlyContinue
+
+$env:IDF_PATH = "C:\Users\ruv\esp\v5.4\esp-idf"
+$env:IDF_TOOLS_PATH = "C:\Espressif\tools"
+$env:IDF_PYTHON_ENV_PATH = "C:\Espressif\tools\python\v5.4\venv"
+$env:PATH = "C:\Espressif\tools\xtensa-esp-elf\esp-14.2.0_20241119\xtensa-esp-elf\bin;C:\Espressif\tools\cmake\3.30.2\cmake-3.30.2-windows-x86_64\bin;C:\Espressif\tools\ninja\1.12.1;C:\Espressif\tools\ccache\4.10.2\ccache-4.10.2-windows-x86_64;C:\Espressif\tools\idf-exe\1.0.3;C:\Espressif\tools\python\v5.4\venv\Scripts;$env:PATH"
+
+Set-Location "C:\Users\ruv\Projects\wifi-densepose\firmware\esp32-csi-node"
+
+$python = "$env:IDF_PYTHON_ENV_PATH\Scripts\python.exe"
+$idf = "$env:IDF_PATH\tools\idf.py"
+
+Write-Host "=== Cleaning stale build cache ==="
+& $python $idf fullclean
+
+Write-Host "=== Building firmware (SSID=ruv.net, target=192.168.1.20:5005) ==="
+& $python $idf build
+
+if ($LASTEXITCODE -eq 0) {
+    Write-Host "=== Build succeeded! Flashing to COM7 ==="
+    & $python $idf -p COM7 flash
+} else {
+    Write-Host "=== Build failed with exit code $LASTEXITCODE ==="
+}
@@ -21,6 +21,16 @@
 #include "esp_timer.h"
 #include "sdkconfig.h"

+/* ADR-057: Build-time guard — fail early if CSI is not enabled in sdkconfig.
+ * Without this, the firmware compiles but crashes at runtime with:
+ *   "E (xxxx) wifi:CSI not enabled in menuconfig!"
+ * which is confusing for users flashing pre-built binaries. */
+#ifndef CONFIG_ESP_WIFI_CSI_ENABLED
+#error "CONFIG_ESP_WIFI_CSI_ENABLED must be set in sdkconfig. " \
+       "Run: idf.py menuconfig -> Component config -> Wi-Fi -> Enable WiFi CSI, " \
+       "or copy sdkconfig.defaults.template to sdkconfig.defaults before building."
+#endif
+
 static const char *TAG = "csi_collector";

 static uint32_t s_sequence = 0;
@@ -0,0 +1,14 @@
+$p = New-Object System.IO.Ports.SerialPort('COM7', 115200)
+$p.ReadTimeout = 5000
+$p.Open()
+Start-Sleep -Milliseconds 200
+
+for ($i = 0; $i -lt 60; $i++) {
+    try {
+        $line = $p.ReadLine()
+        Write-Host $line
+    } catch {
+        break
+    }
+}
+$p.Close()
@@ -0,0 +1,33 @@
+# ESP32-S3 CSI Node — Default SDK Configuration
+# This file is applied automatically by idf.py when no sdkconfig exists.
+
+# Target: ESP32-S3
+CONFIG_IDF_TARGET="esp32s3"
+
+# Use custom partition table (8MB flash with OTA — ADR-045)
+CONFIG_PARTITION_TABLE_CUSTOM=y
+CONFIG_PARTITION_TABLE_CUSTOM_FILENAME="partitions_display.csv"
+
+# Flash configuration: 8MB (Quad SPI)
+CONFIG_ESPTOOLPY_FLASHSIZE_8MB=y
+CONFIG_ESPTOOLPY_FLASHSIZE="8MB"
+
+# Compiler optimization: optimize for size to reduce binary
+CONFIG_COMPILER_OPTIMIZATION_SIZE=y
+
+# Enable CSI (Channel State Information) in WiFi driver
+CONFIG_ESP_WIFI_CSI_ENABLED=y
+
+# NVS encryption disabled by default (requires eFuse provisioning).
+# Enable only after burning HMAC key to eFuse block.
+# CONFIG_NVS_ENCRYPTION is not set
+
+# Disable unused features to reduce binary size
+CONFIG_BOOTLOADER_LOG_LEVEL_WARN=y
+CONFIG_LOG_DEFAULT_LEVEL_INFO=y
+
+# LWIP: enable extended socket options for UDP multicast
+CONFIG_LWIP_SO_RCVBUF=y
+
+# FreeRTOS: increase task stack for CSI processing
+CONFIG_ESP_MAIN_TASK_STACK_SIZE=8192
@@ -18,8 +18,9 @@ CONFIG_COMPILER_OPTIMIZATION_SIZE=y
 # Enable CSI (Channel State Information) in WiFi driver
 CONFIG_ESP_WIFI_CSI_ENABLED=y

-# Enable NVS encryption for secure credential storage
-CONFIG_NVS_ENCRYPTION=y
+# NVS encryption disabled by default (requires eFuse provisioning).
+# Enable only after burning HMAC key to eFuse block.
+# CONFIG_NVS_ENCRYPTION is not set

 # Disable unused features to reduce binary size
 CONFIG_BOOTLOADER_LOG_LEVEL_WARN=y
@@ -4,5 +4,4 @@ pub mod ota;
 pub mod provision;
 pub mod server;
 pub mod settings;
-pub mod training;
 pub mod wasm;
@@ -1,482 +0,0 @@
-//! Training commands for the desktop application.
-//!
-//! Provides Tauri commands for:
-//! - GPU detection
-//! - Dataset management
-//! - Model/checkpoint operations
-//! - Training job control
-//! - RuVector configuration
-//! - Metrics retrieval
-
-use crate::domain::training::{
-    CheckpointInfo, DatasetFormat, DatasetInfo, EpochMetrics, EvaluationMetrics,
-    GpuBackend, GpuInfo, JointAccuracy, LiveTestMetrics,
-    ModelInfo, ModelType, RuVectorConfig, TrainingConfig, TrainingJob,
-    TrainingProgress, TrainingStatus,
-};
-use crate::state::AppState;
-use tauri::State;
-
-// ============================================================================
-// Standard Datasets (built-in)
-// ============================================================================
-
-fn get_standard_datasets() -> Vec<DatasetInfo> {
-    vec![
-        DatasetInfo {
-            id: "mmfi".into(),
-            name: "MM-Fi Dataset".into(),
-            description: "Multi-modal WiFi sensing dataset with 40 subjects, 27 activities".into(),
-            format: DatasetFormat::MmFi,
-            size_mb: 2400.0,
-            samples: 320000,
-            downloaded: false,
-            path: None,
-            url: Some("https://ntu-aiot-lab.github.io/mm-fi".into()),
-        },
-        DatasetInfo {
-            id: "wipose".into(),
-            name: "Wi-Pose Dataset".into(),
-            description: "WiFi-based pose estimation with 3D skeleton annotations".into(),
-            format: DatasetFormat::WiPose,
-            size_mb: 1800.0,
-            samples: 150000,
-            downloaded: false,
-            path: None,
-            url: Some("https://github.com/Wi-Pose".into()),
-        },
-        DatasetInfo {
-            id: "wiar".into(),
-            name: "WiAR Dataset".into(),
-            description: "WiFi activity recognition with CSI data".into(),
-            format: DatasetFormat::Wiar,
-            size_mb: 500.0,
-            samples: 45000,
-            downloaded: false,
-            path: None,
-            url: Some("https://github.com/WiAR".into()),
-        },
-    ]
-}
-
-// ============================================================================
-// Standard Model Architectures
-// ============================================================================
-
-fn get_standard_models() -> Vec<ModelInfo> {
-    vec![
-        ModelInfo {
-            id: "csi-encoder-cnn".into(),
-            name: "CSI Encoder (CNN)".into(),
-            model_type: ModelType::Encoder,
-            description: "Convolutional encoder for CSI amplitude/phase features".into(),
-            params_m: 2.3,
-            memory_mb: 128,
-            paper: None,
-        },
-        ModelInfo {
-            id: "csi-encoder-transformer".into(),
-            name: "CSI Encoder (Transformer)".into(),
-            model_type: ModelType::Encoder,
-            description: "Self-attention based CSI feature extraction".into(),
-            params_m: 8.5,
-            memory_mb: 384,
-            paper: Some("WiFi-ViT 2024".into()),
-        },
-        ModelInfo {
-            id: "pose-decoder-lstm".into(),
-            name: "Pose Decoder (LSTM)".into(),
-            model_type: ModelType::Decoder,
-            description: "Recurrent decoder for temporal pose estimation".into(),
-            params_m: 1.8,
-            memory_mb: 96,
-            paper: None,
-        },
-        ModelInfo {
-            id: "pose-decoder-gru".into(),
-            name: "Pose Decoder (GRU)".into(),
-            model_type: ModelType::Decoder,
-            description: "Gated recurrent unit pose decoder (faster)".into(),
-            params_m: 1.2,
-            memory_mb: 64,
-            paper: None,
-        },
-        ModelInfo {
-            id: "aether-embedding".into(),
-            name: "AETHER Embedding".into(),
-            model_type: ModelType::Embedding,
-            description: "Contrastive CSI embedding for person re-identification (ADR-024)".into(),
-            params_m: 4.2,
-            memory_mb: 192,
-            paper: Some("AETHER 2025".into()),
-        },
-        ModelInfo {
-            id: "meridian-adaptor".into(),
-            name: "MERIDIAN Adaptor".into(),
-            model_type: ModelType::Adaptor,
-            description: "Cross-environment domain generalization module (ADR-027)".into(),
-            params_m: 3.1,
-            memory_mb: 144,
-            paper: Some("MERIDIAN 2025".into()),
-        },
-    ]
-}
-
-// ============================================================================
-// GPU Detection Commands
-// ============================================================================
-
-/// Detect available GPU(s) and return information.
-#[tauri::command]
-pub async fn detect_gpu(state: State<'_, AppState>) -> Result<GpuInfo, String> {
-    // Check for cached GPU info
-    if let Ok(training) = state.training.lock() {
-        if let Some(ref info) = training.gpu_info {
-            return Ok(info.clone());
-        }
-    }
-
-    // Detect GPU
-    let info = detect_gpu_internal();
-
-    // Cache the result
-    if let Ok(mut training) = state.training.lock() {
-        training.gpu_info = Some(info.clone());
-    }
-
-    Ok(info)
-}
-
-fn detect_gpu_internal() -> GpuInfo {
-    // Check for Metal on macOS
-    #[cfg(target_os = "macos")]
-    {
-        // Check if system has Apple Silicon or discrete GPU
-        let has_metal = std::process::Command::new("system_profiler")
-            .args(["SPDisplaysDataType", "-json"])
-            .output()
-            .map(|o| {
-                let output = String::from_utf8_lossy(&o.stdout);
-                output.contains("Metal") || output.contains("Apple M")
-            })
-            .unwrap_or(false);
-
-        if has_metal {
-            // Try to get GPU name
-            let name = std::process::Command::new("system_profiler")
-                .args(["SPDisplaysDataType"])
-                .output()
-                .ok()
-                .and_then(|o| {
-                    let output = String::from_utf8_lossy(&o.stdout);
-                    // Parse chipset name
-                    for line in output.lines() {
-                        if line.contains("Chipset Model:") {
-                            return line.split(':').nth(1).map(|s| s.trim().to_string());
-                        }
-                    }
-                    None
-                });
-
-            return GpuInfo {
-                available: true,
-                backend: GpuBackend::Metal,
-                name,
-                memory_mb: None, // Metal doesn't easily expose this
-                cuda_version: None,
-                metal_supported: true,
-            };
-        }
-    }
-
-    // Check for CUDA on Linux/Windows
-    #[cfg(any(target_os = "linux", target_os = "windows"))]
-    {
-        // Try nvidia-smi for CUDA detection
-        if let Ok(output) = std::process::Command::new("nvidia-smi")
-            .args(["--query-gpu=name,memory.total", "--format=csv,noheader,nounits"])
-            .output()
-        {
-            if output.status.success() {
-                let stdout = String::from_utf8_lossy(&output.stdout);
-                let parts: Vec<&str> = stdout.trim().split(',').collect();
-
-                let name = parts.first().map(|s| s.trim().to_string());
-                let memory_mb = parts.get(1)
-                    .and_then(|s| s.trim().parse::<u64>().ok());
-
-                // Get CUDA version
-                let cuda_version = std::process::Command::new("nvidia-smi")
-                    .output()
-                    .ok()
-                    .and_then(|o| {
-                        let output = String::from_utf8_lossy(&o.stdout);
-                        for line in output.lines() {
-                            if line.contains("CUDA Version:") {
-                                return line.split("CUDA Version:")
-                                    .nth(1)
-                                    .map(|s| s.split_whitespace().next().unwrap_or("").to_string());
-                            }
-                        }
-                        None
-                    });
-
-                return GpuInfo {
-                    available: true,
-                    backend: GpuBackend::Cuda,
-                    name,
-                    memory_mb,
-                    cuda_version,
-                    metal_supported: false,
-                };
-            }
-        }
-    }
-
-    // Fall back to CPU
-    GpuInfo {
-        available: false,
-        backend: GpuBackend::Cpu,
-        name: None,
-        memory_mb: None,
-        cuda_version: None,
-        metal_supported: false,
-    }
-}
-
-// ============================================================================
-// Dataset Commands
-// ============================================================================
-
-/// List available datasets (both standard and downloaded).
-#[tauri::command]
-pub async fn list_datasets(state: State<'_, AppState>) -> Result<Vec<String>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-
-    // Return IDs of downloaded datasets
-    Ok(training.datasets.iter()
-        .filter(|d| d.downloaded)
-        .map(|d| d.id.clone())
-        .collect())
-}
-
-/// Get full dataset information.
-#[tauri::command]
-pub async fn get_datasets(state: State<'_, AppState>) -> Result<Vec<DatasetInfo>, String> {
-    let mut training = state.training.lock().map_err(|e| e.to_string())?;
-
-    // Initialize with standard datasets if empty
-    if training.datasets.is_empty() {
-        training.datasets = get_standard_datasets();
-    }
-
-    Ok(training.datasets.clone())
-}
-
-/// Download a dataset (placeholder - actual download would need async HTTP).
-#[tauri::command]
-pub async fn download_dataset(
-    dataset_id: String,
-    state: State<'_, AppState>,
-) -> Result<DatasetInfo, String> {
-    let mut training = state.training.lock().map_err(|e| e.to_string())?;
-
-    // Find the dataset
-    let dataset = training.datasets.iter_mut()
-        .find(|d| d.id == dataset_id)
-        .ok_or_else(|| format!("Dataset not found: {}", dataset_id))?;
-
-    // Simulate download completion
-    dataset.downloaded = true;
-    dataset.path = Some(format!("~/.ruview/datasets/{}", dataset_id));
-
-    Ok(dataset.clone())
-}
-
-// ============================================================================
-// Model/Checkpoint Commands
-// ============================================================================
-
-/// List available model architectures.
-#[tauri::command]
-pub async fn list_models() -> Result<Vec<ModelInfo>, String> {
-    Ok(get_standard_models())
-}
-
-/// List saved checkpoints.
-#[tauri::command]
-pub async fn list_checkpoints(state: State<'_, AppState>) -> Result<Vec<CheckpointInfo>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.checkpoints.clone())
-}
-
-/// Export a model checkpoint to ONNX or TorchScript.
-#[tauri::command]
-pub async fn export_model(
-    checkpoint_id: String,
-    format: String,
-    state: State<'_, AppState>,
-) -> Result<String, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-
-    let checkpoint = training.checkpoints.iter()
-        .find(|c| c.id == checkpoint_id)
-        .ok_or_else(|| format!("Checkpoint not found: {}", checkpoint_id))?;
-
-    let output_path = match format.as_str() {
-        "onnx" => format!("{}.onnx", checkpoint.path.trim_end_matches(".pt")),
-        "torchscript" => format!("{}.ts", checkpoint.path.trim_end_matches(".pt")),
-        _ => return Err(format!("Unsupported format: {}", format)),
-    };
-
-    // In a real implementation, this would call the actual export logic
-    Ok(output_path)
-}
-
-// ============================================================================
-// Training Job Commands
-// ============================================================================
-
-/// Start a training job.
-#[tauri::command]
-pub async fn start_training(
-    config: TrainingConfig,
-    state: State<'_, AppState>,
-) -> Result<String, String> {
-    let mut training = state.training.lock().map_err(|e| e.to_string())?;
-
-    // Create a new job
-    let job_id = uuid::Uuid::new_v4().to_string();
-    let job = TrainingJob {
-        id: job_id.clone(),
-        config,
-        status: TrainingStatus::Running,
-        started_at: Some(chrono::Utc::now().to_rfc3339()),
-        progress: TrainingProgress::default(),
-        loss_history: Vec::new(),
-    };
-
-    training.current_job = Some(job);
-
-    // In a real implementation, this would spawn a background training thread
-    // and emit progress events via Tauri's event system
-
-    Ok(job_id)
-}
-
-/// Stop the current training job.
-#[tauri::command]
-pub async fn stop_training(state: State<'_, AppState>) -> Result<(), String> {
-    let mut training = state.training.lock().map_err(|e| e.to_string())?;
-
-    if let Some(ref mut job) = training.current_job {
-        job.status = TrainingStatus::Paused;
-    }
-
-    Ok(())
-}
-
-/// Get current training progress.
-#[tauri::command]
-pub async fn training_progress(state: State<'_, AppState>) -> Result<Option<TrainingProgress>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.current_job.as_ref().map(|j| j.progress.clone()))
-}
-
-// ============================================================================
-// RuVector Configuration Commands
-// ============================================================================
-
-/// Get current RuVector configuration.
-#[tauri::command]
-pub async fn get_ruvector_config(state: State<'_, AppState>) -> Result<RuVectorConfig, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.ruvector_config.clone())
-}
-
-/// Set RuVector configuration.
-#[tauri::command]
-pub async fn set_ruvector_config(
-    config: RuVectorConfig,
-    state: State<'_, AppState>,
-) -> Result<(), String> {
-    let mut training = state.training.lock().map_err(|e| e.to_string())?;
-    training.ruvector_config = config;
-    Ok(())
-}
-
-/// Test RuVector modules on live CSI data.
-#[tauri::command]
-pub async fn test_ruvector_live(
-    _state: State<'_, AppState>,
-) -> Result<LiveTestMetrics, String> {
-    // In a real implementation, this would process live CSI data
-    // through the RuVector pipeline and return metrics
-    Ok(LiveTestMetrics {
-        fps: 30.0,
-        latency_ms: 15.0,
-        persons_detected: 1,
-    })
-}
-
-// ============================================================================
-// Metrics Commands
-// ============================================================================
-
-/// Get training history (loss/accuracy per epoch).
-#[tauri::command]
-pub async fn get_training_history(state: State<'_, AppState>) -> Result<Vec<EpochMetrics>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.training_history.clone())
-}
-
-/// Get evaluation metrics.
-#[tauri::command]
-pub async fn get_evaluation_metrics(state: State<'_, AppState>) -> Result<Option<EvaluationMetrics>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.evaluation_metrics.clone())
-}
-
-/// Get per-joint accuracy metrics.
-#[tauri::command]
-pub async fn get_joint_accuracies(state: State<'_, AppState>) -> Result<Vec<JointAccuracy>, String> {
-    let training = state.training.lock().map_err(|e| e.to_string())?;
-    Ok(training.joint_accuracies.clone())
-}
-
-// ============================================================================
-// Tests
-// ============================================================================
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_standard_datasets() {
-        let datasets = get_standard_datasets();
-        assert_eq!(datasets.len(), 3);
-        assert!(datasets.iter().any(|d| d.id == "mmfi"));
-    }
-
-    #[test]
-    fn test_standard_models() {
-        let models = get_standard_models();
-        assert_eq!(models.len(), 6);
-        assert!(models.iter().any(|m| m.id == "csi-encoder-cnn"));
-    }
-
-    #[test]
-    fn test_detect_gpu_internal() {
-        let info = detect_gpu_internal();
-        // Just verify it returns valid data
-        assert!(matches!(info.backend, GpuBackend::Cpu | GpuBackend::Cuda | GpuBackend::Metal));
-    }
-
-    #[test]
-    fn test_ruvector_config_default() {
-        let config = RuVectorConfig::default();
-        assert!(config.mincut_enabled);
-        assert_eq!(config.attention_heads, 4);
-    }
-}
@@ -1,4 +1,3 @@
 pub mod config;
 pub mod firmware;
 pub mod node;
-pub mod training;
@@ -1,312 +0,0 @@
-//! Training domain types for the desktop application.
-
-use serde::{Deserialize, Serialize};
-
-/// GPU backend type.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum GpuBackend {
-    Cuda,
-    Metal,
-    #[default]
-    Cpu,
-}
-
-/// GPU information.
-#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-pub struct GpuInfo {
-    pub available: bool,
-    pub backend: GpuBackend,
-    pub name: Option<String>,
-    pub memory_mb: Option<u64>,
-    pub cuda_version: Option<String>,
-    pub metal_supported: bool,
-}
-
-/// Dataset format type.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum DatasetFormat {
-    #[default]
-    MmFi,
-    WiPose,
-    Wiar,
-    Custom,
-}
-
-/// Dataset information.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct DatasetInfo {
-    pub id: String,
-    pub name: String,
-    pub description: String,
-    pub format: DatasetFormat,
-    pub size_mb: f64,
-    pub samples: u64,
-    pub downloaded: bool,
-    pub path: Option<String>,
-    pub url: Option<String>,
-}
-
-/// Model architecture type.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum ModelType {
-    #[default]
-    Encoder,
-    Decoder,
-    Embedding,
-    Adaptor,
-}
-
-/// Model architecture information.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct ModelInfo {
-    pub id: String,
-    pub name: String,
-    pub model_type: ModelType,
-    pub description: String,
-    pub params_m: f64,
-    pub memory_mb: u64,
-    pub paper: Option<String>,
-}
-
-/// Checkpoint information.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct CheckpointInfo {
-    pub id: String,
-    pub model_id: String,
-    pub name: String,
-    pub epoch: u32,
-    pub val_loss: f64,
-    pub created_at: String,
-    pub path: String,
-    pub size_mb: f64,
-}
-
-/// Training configuration.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct TrainingConfig {
-    pub dataset_id: String,
-    pub model_id: String,
-    pub epochs: u32,
-    pub batch_size: u32,
-    pub learning_rate: f64,
-    pub optimizer: OptimizerType,
-    pub weight_decay: f64,
-    pub use_augmentation: bool,
-    pub checkpoint_every: u32,
-}
-
-impl Default for TrainingConfig {
-    fn default() -> Self {
-        Self {
-            dataset_id: "mmfi".into(),
-            model_id: "csi-encoder-cnn".into(),
-            epochs: 100,
-            batch_size: 32,
-            learning_rate: 0.001,
-            optimizer: OptimizerType::Adam,
-            weight_decay: 0.0001,
-            use_augmentation: true,
-            checkpoint_every: 10,
-        }
-    }
-}
-
-/// Optimizer type.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum OptimizerType {
-    #[default]
-    Adam,
-    AdamW,
-    Sgd,
-}
-
-/// Training job status.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum TrainingStatus {
-    #[default]
-    Pending,
-    Running,
-    Paused,
-    Completed,
-    Failed,
-}
-
-/// Training progress.
-#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-pub struct TrainingProgress {
-    pub epoch: u32,
-    pub total_epochs: u32,
-    pub batch: u32,
-    pub total_batches: u32,
-    pub train_loss: f64,
-    pub val_loss: Option<f64>,
-    pub learning_rate: f64,
-    pub eta_secs: u64,
-    pub gpu_memory_mb: Option<u64>,
-}
-
-/// Training job.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct TrainingJob {
-    pub id: String,
-    pub config: TrainingConfig,
-    pub status: TrainingStatus,
-    pub started_at: Option<String>,
-    pub progress: TrainingProgress,
-    pub loss_history: Vec<EpochMetrics>,
-}
-
-/// Metrics for a single epoch.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct EpochMetrics {
-    pub epoch: u32,
-    pub train_loss: f64,
-    pub val_loss: f64,
-    pub train_acc: f64,
-    pub val_acc: f64,
-    pub learning_rate: f64,
-    pub timestamp: String,
-}
-
-/// Evaluation metrics.
-#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-pub struct EvaluationMetrics {
-    pub pck_05: f64,
-    pub pck_10: f64,
-    pub pck_20: f64,
-    pub map_50: f64,
-    pub map_75: f64,
-    pub iou: f64,
-}
-
-/// Per-joint accuracy.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct JointAccuracy {
-    pub joint: String,
-    pub accuracy: f64,
-}
-
-/// RuVector interpolation mode.
-#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Default)]
-#[serde(rename_all = "lowercase")]
-pub enum InterpolationMode {
-    Linear,
-    Cubic,
-    #[default]
-    Sparse,
-}
-
-/// RuVector module configuration.
-#[derive(Debug, Clone, Serialize, Deserialize)]
-pub struct RuVectorConfig {
-    // MinCut parameters
-    pub mincut_enabled: bool,
-    pub mincut_threshold: f64,
-    pub mincut_max_persons: u32,
-
-    // Attention parameters
-    pub attention_enabled: bool,
-    pub attention_heads: u32,
-    pub attention_dropout: f64,
-
-    // Temporal parameters
-    pub temporal_enabled: bool,
-    pub temporal_window_ms: u32,
-    pub temporal_compression_ratio: u32,
-
-    // Solver parameters
-    pub solver_enabled: bool,
-    pub solver_interpolation: InterpolationMode,
-    pub solver_subcarrier_count: u32,
-
-    // BVP parameters
-    pub bvp_enabled: bool,
-    pub bvp_filter_hz: (f64, f64),
-}
-
-impl Default for RuVectorConfig {
-    fn default() -> Self {
-        Self {
-            mincut_enabled: true,
-            mincut_threshold: 0.5,
-            mincut_max_persons: 5,
-            attention_enabled: true,
-            attention_heads: 4,
-            attention_dropout: 0.1,
-            temporal_enabled: true,
-            temporal_window_ms: 500,
-            temporal_compression_ratio: 4,
-            solver_enabled: true,
-            solver_interpolation: InterpolationMode::Sparse,
-            solver_subcarrier_count: 56,
-            bvp_enabled: false,
-            bvp_filter_hz: (0.7, 4.0),
-        }
-    }
-}
-
-/// Live test metrics from RuVector processing.
-#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-pub struct LiveTestMetrics {
-    pub fps: f64,
-    pub latency_ms: f64,
-    pub persons_detected: u32,
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn test_gpu_info_default() {
-        let info = GpuInfo::default();
-        assert!(!info.available);
-        assert_eq!(info.backend, GpuBackend::Cpu);
-    }
-
-    #[test]
-    fn test_training_config_default() {
-        let config = TrainingConfig::default();
-        assert_eq!(config.epochs, 100);
-        assert_eq!(config.batch_size, 32);
-        assert_eq!(config.optimizer, OptimizerType::Adam);
-    }
-
-    #[test]
-    fn test_ruvector_config_default() {
-        let config = RuVectorConfig::default();
-        assert!(config.mincut_enabled);
-        assert_eq!(config.mincut_threshold, 0.5);
-        assert_eq!(config.attention_heads, 4);
-    }
-
-    #[test]
-    fn test_serialization() {
-        let config = TrainingConfig::default();
-        let json = serde_json::to_string(&config).unwrap();
-        let parsed: TrainingConfig = serde_json::from_str(&json).unwrap();
-        assert_eq!(parsed.epochs, config.epochs);
-    }
-
-    #[test]
-    fn test_dataset_info() {
-        let dataset = DatasetInfo {
-            id: "mmfi".into(),
-            name: "MM-Fi Dataset".into(),
-            description: "Multi-modal WiFi sensing".into(),
-            format: DatasetFormat::MmFi,
-            size_mb: 2400.0,
-            samples: 320000,
-            downloaded: false,
-            path: None,
-            url: Some("https://example.com/mmfi.zip".into()),
-        };
-        assert_eq!(dataset.id, "mmfi");
-        assert!(!dataset.downloaded);
-    }
-}
@@ -2,7 +2,7 @@ pub mod commands;
 pub mod domain;
 pub mod state;

-use commands::{discovery, flash, ota, provision, server, settings, training, wasm};
+use commands::{discovery, flash, ota, provision, server, settings, wasm};

 pub fn run() {
    tauri::Builder::default()
@@ -46,23 +46,6 @@ pub fn run() {
            // Settings
            settings::get_settings,
            settings::save_settings,
-            // Training
-            training::detect_gpu,
-            training::list_datasets,
-            training::get_datasets,
-            training::download_dataset,
-            training::list_models,
-            training::list_checkpoints,
-            training::export_model,
-            training::start_training,
-            training::stop_training,
-            training::training_progress,
-            training::get_ruvector_config,
-            training::set_ruvector_config,
-            training::test_ruvector_live,
-            training::get_training_history,
-            training::get_evaluation_metrics,
-            training::get_joint_accuracies,
        ])
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
@@ -3,10 +3,6 @@ use std::sync::Mutex;
 use std::time::Instant;

 use crate::domain::node::DiscoveredNode;
-use crate::domain::training::{
-    CheckpointInfo, DatasetInfo, EpochMetrics, EvaluationMetrics,
-    GpuInfo, JointAccuracy, RuVectorConfig, TrainingJob,
-};

 /// Sub-state for discovered nodes.
 #[derive(Default)]
@@ -91,33 +87,6 @@ impl Default for SettingsState {
    }
 }

-/// Sub-state for training operations.
-pub struct TrainingState {
-    pub gpu_info: Option<GpuInfo>,
-    pub datasets: Vec<DatasetInfo>,
-    pub checkpoints: Vec<CheckpointInfo>,
-    pub current_job: Option<TrainingJob>,
-    pub ruvector_config: RuVectorConfig,
-    pub training_history: Vec<EpochMetrics>,
-    pub evaluation_metrics: Option<EvaluationMetrics>,
-    pub joint_accuracies: Vec<JointAccuracy>,
-}
-
-impl Default for TrainingState {
-    fn default() -> Self {
-        Self {
-            gpu_info: None,
-            datasets: Vec::new(),
-            checkpoints: Vec::new(),
-            current_job: None,
-            ruvector_config: RuVectorConfig::default(),
-            training_history: Vec::new(),
-            evaluation_metrics: None,
-            joint_accuracies: Vec::new(),
-        }
-    }
-}
-
 /// Top-level application state managed by Tauri.
 pub struct AppState {
    pub discovery: Mutex<DiscoveryState>,
@@ -125,7 +94,6 @@ pub struct AppState {
    pub flash: Mutex<FlashState>,
    pub ota: Mutex<OtaState>,
    pub settings: Mutex<SettingsState>,
-    pub training: Mutex<TrainingState>,
 }

 impl Default for AppState {
@@ -136,7 +104,6 @@ impl Default for AppState {
            flash: Mutex::new(FlashState::default()),
            ota: Mutex::new(OtaState::default()),
            settings: Mutex::new(SettingsState::default()),
-            training: Mutex::new(TrainingState::default()),
        }
    }
 }
@@ -168,9 +135,6 @@ impl AppState {
        if let Ok(mut settings) = self.settings.lock() {
            *settings = SettingsState::default();
        }
-        if let Ok(mut training) = self.training.lock() {
-            *training = TrainingState::default();
-        }
    }
 }

@@ -1,7 +1,7 @@
 {
  "$schema": "https://raw.githubusercontent.com/tauri-apps/tauri/dev/crates/tauri-config-schema/schema.json",
  "productName": "RuView Desktop",
-  "version": "0.5.0",
+  "version": "0.4.4",
  "identifier": "net.ruv.ruview",
  "build": {
    "frontendDist": "ui/dist",
@@ -1,7 +1,7 @@
 {
  "name": "ruview-desktop-ui",
  "private": true,
-  "version": "0.5.0",
+  "version": "0.4.4",
  "type": "module",
  "scripts": {
    "dev": "vite",
@@ -8,7 +8,6 @@ import { OtaUpdate } from "./pages/OtaUpdate";
 import { EdgeModules } from "./pages/EdgeModules";
 import { Sensing } from "./pages/Sensing";
 import { MeshView } from "./pages/MeshView";
-import Training from "./pages/Training";
 import { Settings } from "./pages/Settings";

 type Page =
@@ -20,7 +19,6 @@ type Page =
  | "wasm"
  | "sensing"
  | "mesh"
-  | "training"
  | "settings";

 interface NavItem {
@@ -38,7 +36,6 @@ const NAV_ITEMS: NavItem[] = [
  { id: "wasm", label: "Edge Modules", icon: "\u2B21" },
  { id: "sensing", label: "Sensing", icon: "\u2248" },
  { id: "mesh", label: "Mesh View", icon: "\u2B2F" },
-  { id: "training", label: "Training", icon: "\u2B50" },
  { id: "settings", label: "Settings", icon: "\u2699" },
 ];

@@ -102,7 +99,6 @@ const App: React.FC = () => {
      case "wasm": return <EdgeModules />;
      case "sensing": return <Sensing />;
      case "mesh": return <MeshView />;
-      case "training": return <Training />;
      case "settings": return <Settings />;
    }
  };
@@ -1,369 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-
-interface Dataset {
-  id: string;
-  name: string;
-  description: string;
-  size_mb: number;
-  samples: number;
-  downloaded: boolean;
-  path: string | null;
-}
-
-const STANDARD_DATASETS: Omit<Dataset, "downloaded" | "path">[] = [
-  {
-    id: "mmfi",
-    name: "MM-Fi Dataset",
-    description: "Multi-modal WiFi sensing dataset with 40 subjects, 27 activities",
-    size_mb: 2400,
-    samples: 320000,
-  },
-  {
-    id: "wipose",
-    name: "Wi-Pose Dataset",
-    description: "WiFi-based pose estimation with 3D skeleton annotations",
-    size_mb: 1800,
-    samples: 150000,
-  },
-  {
-    id: "wiar",
-    name: "WiAR Dataset",
-    description: "WiFi activity recognition with CSI data",
-    size_mb: 500,
-    samples: 45000,
-  },
-];
-
-const DatasetsTab: React.FC = () => {
-  const [datasets, setDatasets] = useState<Dataset[]>([]);
-  const [downloading, setDownloading] = useState<string | null>(null);
-  const [downloadProgress, setDownloadProgress] = useState<number>(0);
-  const [error, setError] = useState<string | null>(null);
-
-  useEffect(() => {
-    loadDatasets();
-  }, []);
-
-  const loadDatasets = async () => {
-    try {
-      const downloaded = await invoke<string[]>("list_datasets");
-      const ds = STANDARD_DATASETS.map((d) => ({
-        ...d,
-        downloaded: downloaded.includes(d.id),
-        path: downloaded.includes(d.id) ? `~/.ruview/datasets/${d.id}` : null,
-      }));
-      setDatasets(ds);
-    } catch (err) {
-      // If command not implemented yet, show placeholders
-      setDatasets(
-        STANDARD_DATASETS.map((d) => ({
-          ...d,
-          downloaded: false,
-          path: null,
-        }))
-      );
-    }
-  };
-
-  const handleDownload = async (datasetId: string) => {
-    setDownloading(datasetId);
-    setDownloadProgress(0);
-    setError(null);
-
-    try {
-      // Simulate download progress for now
-      for (let i = 0; i <= 100; i += 10) {
-        setDownloadProgress(i);
-        await new Promise((r) => setTimeout(r, 500));
-      }
-
-      // TODO: Call actual download command
-      // await invoke("download_dataset", { datasetId });
-
-      setDatasets((prev) =>
-        prev.map((d) =>
-          d.id === datasetId
-            ? { ...d, downloaded: true, path: `~/.ruview/datasets/${d.id}` }
-            : d
-        )
-      );
-    } catch (err) {
-      setError(`Download failed: ${err}`);
-    } finally {
-      setDownloading(null);
-    }
-  };
-
-  return (
-    <div>
-      {/* Stats Row */}
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(3, 1fr)",
-          gap: "var(--space-4)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        <StatCard
-          label="Available Datasets"
-          value={datasets.length}
-        />
-        <StatCard
-          label="Downloaded"
-          value={datasets.filter((d) => d.downloaded).length}
-          color="var(--status-online)"
-        />
-        <StatCard
-          label="Total Samples"
-          value={`${(datasets.reduce((acc, d) => acc + (d.downloaded ? d.samples : 0), 0) / 1000).toFixed(0)}K`}
-        />
-      </div>
-
-      {error && (
-        <div
-          style={{
-            background: "rgba(248, 81, 73, 0.1)",
-            border: "1px solid rgba(248, 81, 73, 0.3)",
-            borderRadius: 6,
-            padding: "var(--space-3)",
-            marginBottom: "var(--space-4)",
-            fontSize: 13,
-            color: "var(--status-error)",
-          }}
-        >
-          {error}
-        </div>
-      )}
-
-      {/* Dataset Cards */}
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(auto-fill, minmax(340px, 1fr))",
-          gap: "var(--space-4)",
-        }}
-      >
-        {datasets.map((dataset) => (
-          <div
-            key={dataset.id}
-            className="card"
-            style={{
-              padding: "var(--space-4)",
-              opacity: dataset.downloaded ? 1 : 0.85,
-            }}
-          >
-            <div
-              style={{
-                display: "flex",
-                justifyContent: "space-between",
-                alignItems: "start",
-                marginBottom: "var(--space-3)",
-              }}
-            >
-              <div>
-                <h3 style={{ margin: 0, fontSize: 15, fontWeight: 600 }}>
-                  {dataset.name}
-                </h3>
-                <p
-                  style={{
-                    fontSize: 12,
-                    color: "var(--text-muted)",
-                    marginTop: 4,
-                    lineHeight: 1.4,
-                  }}
-                >
-                  {dataset.description}
-                </p>
-              </div>
-              {dataset.downloaded && (
-                <span
-                  style={{
-                    background: "rgba(63, 185, 80, 0.15)",
-                    color: "var(--status-online)",
-                    padding: "2px 8px",
-                    borderRadius: 4,
-                    fontSize: 10,
-                    fontWeight: 600,
-                  }}
-                >
-                  DOWNLOADED
-                </span>
-              )}
-            </div>
-
-            <div
-              style={{
-                display: "flex",
-                gap: "var(--space-4)",
-                fontSize: 12,
-                color: "var(--text-secondary)",
-                marginBottom: "var(--space-3)",
-              }}
-            >
-              <span>📦 {(dataset.size_mb / 1024).toFixed(1)} GB</span>
-              <span>📊 {(dataset.samples / 1000).toFixed(0)}K samples</span>
-            </div>
-
-            {downloading === dataset.id ? (
-              <div>
-                <div
-                  style={{
-                    height: 4,
-                    background: "var(--border)",
-                    borderRadius: 2,
-                    overflow: "hidden",
-                  }}
-                >
-                  <div
-                    style={{
-                      width: `${downloadProgress}%`,
-                      height: "100%",
-                      background: "var(--accent)",
-                      transition: "width 0.3s",
-                    }}
-                  />
-                </div>
-                <div
-                  style={{
-                    fontSize: 11,
-                    color: "var(--text-muted)",
-                    marginTop: 4,
-                    textAlign: "center",
-                  }}
-                >
-                  Downloading... {downloadProgress}%
-                </div>
-              </div>
-            ) : (
-              <div style={{ display: "flex", gap: "var(--space-2)" }}>
-                {dataset.downloaded ? (
-                  <>
-                    <button
-                      style={{
-                        flex: 1,
-                        padding: "8px 12px",
-                        background: "rgba(56, 139, 253, 0.1)",
-                        border: "1px solid rgba(56, 139, 253, 0.3)",
-                        borderRadius: 6,
-                        color: "var(--accent)",
-                        fontSize: 12,
-                        fontWeight: 600,
-                        cursor: "pointer",
-                      }}
-                    >
-                      Preview
-                    </button>
-                    <button
-                      style={{
-                        padding: "8px 12px",
-                        background: "transparent",
-                        border: "1px solid var(--border)",
-                        borderRadius: 6,
-                        color: "var(--text-secondary)",
-                        fontSize: 12,
-                        fontWeight: 600,
-                        cursor: "pointer",
-                      }}
-                    >
-                      Delete
-                    </button>
-                  </>
-                ) : (
-                  <button
-                    onClick={() => handleDownload(dataset.id)}
-                    className="btn-gradient"
-                    style={{ flex: 1, fontSize: 12 }}
-                  >
-                    Download Dataset
-                  </button>
-                )}
-              </div>
-            )}
-          </div>
-        ))}
-      </div>
-
-      {/* Import Custom Dataset */}
-      <div
-        className="card"
-        style={{
-          marginTop: "var(--space-5)",
-          padding: "var(--space-4)",
-          border: "2px dashed var(--border)",
-          textAlign: "center",
-        }}
-      >
-        <div style={{ fontSize: 32, marginBottom: "var(--space-2)" }}>📁</div>
-        <h4 style={{ margin: 0, fontSize: 14, fontWeight: 600 }}>
-          Import Custom Dataset
-        </h4>
-        <p
-          style={{
-            fontSize: 12,
-            color: "var(--text-muted)",
-            marginTop: 4,
-            marginBottom: "var(--space-3)",
-          }}
-        >
-          Import CSI recordings in CSV, NPZ, or HDF5 format
-        </p>
-        <button
-          style={{
-            padding: "8px 20px",
-            background: "transparent",
-            border: "1px solid var(--border)",
-            borderRadius: 6,
-            color: "var(--text-secondary)",
-            fontSize: 12,
-            fontWeight: 600,
-            cursor: "pointer",
-          }}
-        >
-          Browse Files
-        </button>
-      </div>
-    </div>
-  );
-};
-
-function StatCard({
-  label,
-  value,
-  color,
-}: {
-  label: string;
-  value: number | string;
-  color?: string;
-}) {
-  return (
-    <div className="card-glow" style={{ padding: "var(--space-4)" }}>
-      <div
-        style={{
-          fontSize: 10,
-          textTransform: "uppercase",
-          letterSpacing: "0.06em",
-          color: "var(--text-muted)",
-          marginBottom: "var(--space-2)",
-          fontWeight: 600,
-        }}
-      >
-        {label}
-      </div>
-      <div
-        style={{
-          fontFamily: "var(--font-mono)",
-          fontSize: 28,
-          fontWeight: 600,
-          color: color || "var(--text-primary)",
-          letterSpacing: "-0.02em",
-        }}
-      >
-        {value}
-      </div>
-    </div>
-  );
-}
-
-export default DatasetsTab;
@@ -1,609 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-
-interface TrainingMetrics {
-  epoch: number;
-  train_loss: number;
-  val_loss: number;
-  train_acc: number;
-  val_acc: number;
-  learning_rate: number;
-  timestamp: string;
-}
-
-interface EvaluationMetrics {
-  pck_05: number;
-  pck_10: number;
-  pck_20: number;
-  map_50: number;
-  map_75: number;
-  iou: number;
-}
-
-interface JointAccuracy {
-  joint: string;
-  accuracy: number;
-}
-
-const JOINT_NAMES = [
-  "nose",
-  "left_eye",
-  "right_eye",
-  "left_ear",
-  "right_ear",
-  "left_shoulder",
-  "right_shoulder",
-  "left_elbow",
-  "right_elbow",
-  "left_wrist",
-  "right_wrist",
-  "left_hip",
-  "right_hip",
-  "left_knee",
-  "right_knee",
-  "left_ankle",
-  "right_ankle",
-];
-
-const MetricsTab: React.FC = () => {
-  const [trainingHistory, setTrainingHistory] = useState<TrainingMetrics[]>([]);
-  const [evaluation, setEvaluation] = useState<EvaluationMetrics | null>(null);
-  const [jointAccuracies, setJointAccuracies] = useState<JointAccuracy[]>([]);
-  const [selectedMetric, setSelectedMetric] = useState<"loss" | "accuracy">("loss");
-  const [exporting, setExporting] = useState(false);
-
-  useEffect(() => {
-    loadMetrics();
-  }, []);
-
-  const loadMetrics = async () => {
-    try {
-      const metrics = await invoke<TrainingMetrics[]>("get_training_history");
-      setTrainingHistory(metrics);
-      const evalMetrics = await invoke<EvaluationMetrics>("get_evaluation_metrics");
-      setEvaluation(evalMetrics);
-      const joints = await invoke<JointAccuracy[]>("get_joint_accuracies");
-      setJointAccuracies(joints);
-    } catch (err) {
-      // Generate mock data for demonstration
-      const mockHistory: TrainingMetrics[] = [];
-      for (let i = 1; i <= 50; i++) {
-        mockHistory.push({
-          epoch: i,
-          train_loss: 0.5 * Math.exp(-i / 20) + 0.02 + Math.random() * 0.01,
-          val_loss: 0.55 * Math.exp(-i / 18) + 0.025 + Math.random() * 0.015,
-          train_acc: 1 - 0.5 * Math.exp(-i / 15) - Math.random() * 0.02,
-          val_acc: 1 - 0.55 * Math.exp(-i / 15) - Math.random() * 0.025,
-          learning_rate: 0.001 * Math.pow(0.95, Math.floor(i / 10)),
-          timestamp: new Date(Date.now() - (50 - i) * 60000).toISOString(),
-        });
-      }
-      setTrainingHistory(mockHistory);
-
-      setEvaluation({
-        pck_05: 0.72,
-        pck_10: 0.89,
-        pck_20: 0.96,
-        map_50: 0.84,
-        map_75: 0.71,
-        iou: 0.78,
-      });
-
-      setJointAccuracies(
-        JOINT_NAMES.map((joint) => ({
-          joint,
-          accuracy: 0.7 + Math.random() * 0.25,
-        }))
-      );
-    }
-  };
-
-  const exportMetrics = async (format: "csv" | "json" | "tensorboard") => {
-    setExporting(true);
-    try {
-      if (format === "json") {
-        const data = {
-          training: trainingHistory,
-          evaluation,
-          joints: jointAccuracies,
-        };
-        const blob = new Blob([JSON.stringify(data, null, 2)], { type: "application/json" });
-        downloadBlob(blob, "metrics.json");
-      } else if (format === "csv") {
-        const headers = "epoch,train_loss,val_loss,train_acc,val_acc,learning_rate\n";
-        const rows = trainingHistory
-          .map(
-            (m) =>
-              `${m.epoch},${m.train_loss.toFixed(6)},${m.val_loss.toFixed(6)},${m.train_acc.toFixed(4)},${m.val_acc.toFixed(4)},${m.learning_rate.toExponential(2)}`
-          )
-          .join("\n");
-        const blob = new Blob([headers + rows], { type: "text/csv" });
-        downloadBlob(blob, "training_history.csv");
-      } else {
-        // TensorBoard format would require server-side handling
-        alert("TensorBoard export requires running the backend server");
-      }
-    } finally {
-      setExporting(false);
-    }
-  };
-
-  const downloadBlob = (blob: Blob, filename: string) => {
-    const url = URL.createObjectURL(blob);
-    const a = document.createElement("a");
-    a.href = url;
-    a.download = filename;
-    a.click();
-    URL.revokeObjectURL(url);
-  };
-
-  const maxLoss = Math.max(
-    ...trainingHistory.map((m) => Math.max(m.train_loss, m.val_loss)),
-    0.1
-  );
-
-  return (
-    <div>
-      {/* Summary Stats */}
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(4, 1fr)",
-          gap: "var(--space-4)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        <StatCard
-          label="Epochs Trained"
-          value={trainingHistory.length}
-        />
-        <StatCard
-          label="Best Val Loss"
-          value={
-            trainingHistory.length > 0
-              ? Math.min(...trainingHistory.map((m) => m.val_loss)).toFixed(4)
-              : "—"
-          }
-          color="var(--status-online)"
-        />
-        <StatCard
-          label="Best Val Acc"
-          value={
-            trainingHistory.length > 0
-              ? `${(Math.max(...trainingHistory.map((m) => m.val_acc)) * 100).toFixed(1)}%`
-              : "—"
-          }
-          color="var(--accent)"
-        />
-        <StatCard
-          label="PCK@0.1"
-          value={evaluation ? `${(evaluation.pck_10 * 100).toFixed(1)}%` : "—"}
-        />
-      </div>
-
-      <div style={{ display: "grid", gridTemplateColumns: "2fr 1fr", gap: "var(--space-5)" }}>
-        {/* Loss/Accuracy Charts */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <div
-            style={{
-              display: "flex",
-              justifyContent: "space-between",
-              alignItems: "center",
-              marginBottom: "var(--space-4)",
-            }}
-          >
-            <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600 }}>Training Curves</h3>
-            <div style={{ display: "flex", gap: "var(--space-2)" }}>
-              <button
-                onClick={() => setSelectedMetric("loss")}
-                style={{
-                  padding: "6px 12px",
-                  background: selectedMetric === "loss" ? "var(--accent)" : "transparent",
-                  border: `1px solid ${selectedMetric === "loss" ? "var(--accent)" : "var(--border)"}`,
-                  borderRadius: 4,
-                  color: selectedMetric === "loss" ? "white" : "var(--text-secondary)",
-                  fontSize: 11,
-                  fontWeight: 600,
-                  cursor: "pointer",
-                }}
-              >
-                Loss
-              </button>
-              <button
-                onClick={() => setSelectedMetric("accuracy")}
-                style={{
-                  padding: "6px 12px",
-                  background: selectedMetric === "accuracy" ? "var(--accent)" : "transparent",
-                  border: `1px solid ${selectedMetric === "accuracy" ? "var(--accent)" : "var(--border)"}`,
-                  borderRadius: 4,
-                  color: selectedMetric === "accuracy" ? "white" : "var(--text-secondary)",
-                  fontSize: 11,
-                  fontWeight: 600,
-                  cursor: "pointer",
-                }}
-              >
-                Accuracy
-              </button>
-            </div>
-          </div>
-
-          {/* Chart Area */}
-          <div
-            style={{
-              height: 250,
-              position: "relative",
-              background: "var(--bg-secondary)",
-              borderRadius: 8,
-              padding: "var(--space-3)",
-            }}
-          >
-            {trainingHistory.length === 0 ? (
-              <div
-                style={{
-                  display: "flex",
-                  flexDirection: "column",
-                  alignItems: "center",
-                  justifyContent: "center",
-                  height: "100%",
-                  color: "var(--text-muted)",
-                }}
-              >
-                <span style={{ fontSize: 32 }}>📊</span>
-                <p style={{ fontSize: 13, marginTop: "var(--space-2)" }}>
-                  No training data yet
-                </p>
-              </div>
-            ) : (
-              <svg width="100%" height="100%" viewBox="0 0 500 200" preserveAspectRatio="none">
-                {/* Grid lines */}
-                {[0, 0.25, 0.5, 0.75, 1].map((y) => (
-                  <line
-                    key={y}
-                    x1="0"
-                    y1={y * 180}
-                    x2="500"
-                    y2={y * 180}
-                    stroke="var(--border)"
-                    strokeWidth="0.5"
-                    strokeDasharray="4"
-                  />
-                ))}
-
-                {/* Train line */}
-                <polyline
-                  fill="none"
-                  stroke="var(--accent)"
-                  strokeWidth="2"
-                  points={trainingHistory
-                    .map((m, i) => {
-                      const x = (i / (trainingHistory.length - 1)) * 500;
-                      const value = selectedMetric === "loss" ? m.train_loss : m.train_acc;
-                      const y =
-                        selectedMetric === "loss"
-                          ? (value / maxLoss) * 180
-                          : (1 - value) * 180;
-                      return `${x},${y}`;
-                    })
-                    .join(" ")}
-                />
-
-                {/* Val line */}
-                <polyline
-                  fill="none"
-                  stroke="var(--status-online)"
-                  strokeWidth="2"
-                  points={trainingHistory
-                    .map((m, i) => {
-                      const x = (i / (trainingHistory.length - 1)) * 500;
-                      const value = selectedMetric === "loss" ? m.val_loss : m.val_acc;
-                      const y =
-                        selectedMetric === "loss"
-                          ? (value / maxLoss) * 180
-                          : (1 - value) * 180;
-                      return `${x},${y}`;
-                    })
-                    .join(" ")}
-                />
-              </svg>
-            )}
-
-            {/* Legend */}
-            <div
-              style={{
-                position: "absolute",
-                top: "var(--space-2)",
-                right: "var(--space-2)",
-                display: "flex",
-                gap: "var(--space-3)",
-                fontSize: 11,
-              }}
-            >
-              <span style={{ display: "flex", alignItems: "center", gap: 4 }}>
-                <span
-                  style={{
-                    width: 12,
-                    height: 3,
-                    background: "var(--accent)",
-                    borderRadius: 2,
-                  }}
-                />
-                Train
-              </span>
-              <span style={{ display: "flex", alignItems: "center", gap: 4 }}>
-                <span
-                  style={{
-                    width: 12,
-                    height: 3,
-                    background: "var(--status-online)",
-                    borderRadius: 2,
-                  }}
-                />
-                Validation
-              </span>
-            </div>
-          </div>
-        </div>
-
-        {/* Evaluation Metrics */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-            Evaluation Metrics
-          </h3>
-
-          {!evaluation ? (
-            <div
-              style={{
-                display: "flex",
-                flexDirection: "column",
-                alignItems: "center",
-                justifyContent: "center",
-                height: 200,
-                color: "var(--text-muted)",
-              }}
-            >
-              <span style={{ fontSize: 32 }}>📏</span>
-              <p style={{ fontSize: 13, marginTop: "var(--space-2)" }}>
-                Run evaluation to see metrics
-              </p>
-            </div>
-          ) : (
-            <div style={{ display: "flex", flexDirection: "column", gap: "var(--space-3)" }}>
-              <MetricBar label="PCK@0.05" value={evaluation.pck_05} color="#f59e0b" />
-              <MetricBar label="PCK@0.10" value={evaluation.pck_10} color="var(--accent)" />
-              <MetricBar label="PCK@0.20" value={evaluation.pck_20} color="var(--status-online)" />
-              <div style={{ height: 1, background: "var(--border)", margin: "var(--space-2) 0" }} />
-              <MetricBar label="mAP@0.50" value={evaluation.map_50} color="#a855f7" />
-              <MetricBar label="mAP@0.75" value={evaluation.map_75} color="#ec4899" />
-              <MetricBar label="IoU" value={evaluation.iou} color="#06b6d4" />
-            </div>
-          )}
-        </div>
-      </div>
-
-      {/* Joint-wise Accuracy */}
-      <div className="card" style={{ marginTop: "var(--space-5)", padding: "var(--space-4)" }}>
-        <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-          Per-Joint Accuracy
-        </h3>
-
-        {jointAccuracies.length === 0 ? (
-          <div
-            style={{
-              textAlign: "center",
-              padding: "var(--space-5)",
-              color: "var(--text-muted)",
-            }}
-          >
-            No joint accuracy data available
-          </div>
-        ) : (
-          <div
-            style={{
-              display: "grid",
-              gridTemplateColumns: "repeat(auto-fill, minmax(140px, 1fr))",
-              gap: "var(--space-3)",
-            }}
-          >
-            {jointAccuracies.map((ja) => (
-              <div
-                key={ja.joint}
-                style={{
-                  padding: "var(--space-3)",
-                  background: "var(--bg-secondary)",
-                  borderRadius: 6,
-                  textAlign: "center",
-                }}
-              >
-                <div
-                  style={{
-                    fontSize: 11,
-                    color: "var(--text-muted)",
-                    marginBottom: 4,
-                    textTransform: "capitalize",
-                  }}
-                >
-                  {ja.joint.replace("_", " ")}
-                </div>
-                <div
-                  style={{
-                    fontFamily: "var(--font-mono)",
-                    fontSize: 18,
-                    fontWeight: 600,
-                    color:
-                      ja.accuracy > 0.9
-                        ? "var(--status-online)"
-                        : ja.accuracy > 0.8
-                          ? "var(--accent)"
-                          : ja.accuracy > 0.7
-                            ? "#f59e0b"
-                            : "var(--status-error)",
-                  }}
-                >
-                  {(ja.accuracy * 100).toFixed(1)}%
-                </div>
-              </div>
-            ))}
-          </div>
-        )}
-      </div>
-
-      {/* Export Section */}
-      <div
-        className="card"
-        style={{
-          marginTop: "var(--space-5)",
-          padding: "var(--space-4)",
-          display: "flex",
-          justifyContent: "space-between",
-          alignItems: "center",
-        }}
-      >
-        <div>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600 }}>Export Metrics</h3>
-          <p style={{ fontSize: 12, color: "var(--text-muted)", marginTop: 4 }}>
-            Download training history and evaluation results
-          </p>
-        </div>
-        <div style={{ display: "flex", gap: "var(--space-2)" }}>
-          <button
-            onClick={() => exportMetrics("csv")}
-            disabled={exporting || trainingHistory.length === 0}
-            style={{
-              padding: "8px 16px",
-              background: "rgba(56, 139, 253, 0.1)",
-              border: "1px solid rgba(56, 139, 253, 0.3)",
-              borderRadius: 6,
-              color: "var(--accent)",
-              fontSize: 12,
-              fontWeight: 600,
-              cursor: trainingHistory.length === 0 ? "not-allowed" : "pointer",
-              opacity: trainingHistory.length === 0 ? 0.5 : 1,
-            }}
-          >
-            CSV
-          </button>
-          <button
-            onClick={() => exportMetrics("json")}
-            disabled={exporting || trainingHistory.length === 0}
-            style={{
-              padding: "8px 16px",
-              background: "rgba(56, 139, 253, 0.1)",
-              border: "1px solid rgba(56, 139, 253, 0.3)",
-              borderRadius: 6,
-              color: "var(--accent)",
-              fontSize: 12,
-              fontWeight: 600,
-              cursor: trainingHistory.length === 0 ? "not-allowed" : "pointer",
-              opacity: trainingHistory.length === 0 ? 0.5 : 1,
-            }}
-          >
-            JSON
-          </button>
-          <button
-            onClick={() => exportMetrics("tensorboard")}
-            disabled={exporting || trainingHistory.length === 0}
-            style={{
-              padding: "8px 16px",
-              background: "transparent",
-              border: "1px solid var(--border)",
-              borderRadius: 6,
-              color: "var(--text-secondary)",
-              fontSize: 12,
-              fontWeight: 600,
-              cursor: trainingHistory.length === 0 ? "not-allowed" : "pointer",
-              opacity: trainingHistory.length === 0 ? 0.5 : 1,
-            }}
-          >
-            TensorBoard
-          </button>
-        </div>
-      </div>
-    </div>
-  );
-};
-
-function StatCard({
-  label,
-  value,
-  color,
-}: {
-  label: string;
-  value: number | string;
-  color?: string;
-}) {
-  return (
-    <div className="card-glow" style={{ padding: "var(--space-4)" }}>
-      <div
-        style={{
-          fontSize: 10,
-          textTransform: "uppercase",
-          letterSpacing: "0.06em",
-          color: "var(--text-muted)",
-          marginBottom: "var(--space-2)",
-          fontWeight: 600,
-        }}
-      >
-        {label}
-      </div>
-      <div
-        style={{
-          fontFamily: "var(--font-mono)",
-          fontSize: 28,
-          fontWeight: 600,
-          color: color || "var(--text-primary)",
-          letterSpacing: "-0.02em",
-        }}
-      >
-        {value}
-      </div>
-    </div>
-  );
-}
-
-function MetricBar({
-  label,
-  value,
-  color,
-}: {
-  label: string;
-  value: number;
-  color: string;
-}) {
-  return (
-    <div>
-      <div
-        style={{
-          display: "flex",
-          justifyContent: "space-between",
-          fontSize: 12,
-          marginBottom: 4,
-        }}
-      >
-        <span>{label}</span>
-        <span style={{ fontFamily: "var(--font-mono)", fontWeight: 600 }}>
-          {(value * 100).toFixed(1)}%
-        </span>
-      </div>
-      <div
-        style={{
-          height: 6,
-          background: "var(--bg-secondary)",
-          borderRadius: 3,
-          overflow: "hidden",
-        }}
-      >
-        <div
-          style={{
-            width: `${value * 100}%`,
-            height: "100%",
-            background: color,
-            borderRadius: 3,
-            transition: "width 0.5s",
-          }}
-        />
-      </div>
-    </div>
-  );
-}
-
-export default MetricsTab;
@@ -1,405 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-
-interface ModelArchitecture {
-  id: string;
-  name: string;
-  type: "encoder" | "decoder" | "embedding" | "adaptor";
-  description: string;
-  params_m: number;
-  memory_mb: number;
-  paper?: string;
-}
-
-interface Checkpoint {
-  id: string;
-  model_id: string;
-  name: string;
-  epoch: number;
-  val_loss: number;
-  created_at: string;
-  path: string;
-  size_mb: number;
-}
-
-const MODEL_ARCHITECTURES: ModelArchitecture[] = [
-  {
-    id: "csi-encoder-cnn",
-    name: "CSI Encoder (CNN)",
-    type: "encoder",
-    description: "Convolutional encoder for CSI amplitude/phase features",
-    params_m: 2.3,
-    memory_mb: 128,
-  },
-  {
-    id: "csi-encoder-transformer",
-    name: "CSI Encoder (Transformer)",
-    type: "encoder",
-    description: "Self-attention based CSI feature extraction",
-    params_m: 8.5,
-    memory_mb: 384,
-    paper: "WiFi-ViT 2024",
-  },
-  {
-    id: "pose-decoder-lstm",
-    name: "Pose Decoder (LSTM)",
-    type: "decoder",
-    description: "Recurrent decoder for temporal pose estimation",
-    params_m: 1.8,
-    memory_mb: 96,
-  },
-  {
-    id: "pose-decoder-gru",
-    name: "Pose Decoder (GRU)",
-    type: "decoder",
-    description: "Gated recurrent unit pose decoder (faster)",
-    params_m: 1.2,
-    memory_mb: 64,
-  },
-  {
-    id: "aether-embedding",
-    name: "AETHER Embedding",
-    type: "embedding",
-    description: "Contrastive CSI embedding for person re-identification (ADR-024)",
-    params_m: 4.2,
-    memory_mb: 192,
-    paper: "AETHER 2025",
-  },
-  {
-    id: "meridian-adaptor",
-    name: "MERIDIAN Adaptor",
-    type: "adaptor",
-    description: "Cross-environment domain generalization module (ADR-027)",
-    params_m: 3.1,
-    memory_mb: 144,
-    paper: "MERIDIAN 2025",
-  },
-];
-
-const ModelsTab: React.FC = () => {
-  const [checkpoints, setCheckpoints] = useState<Checkpoint[]>([]);
-  const [selectedModel, setSelectedModel] = useState<string | null>(null);
-  const [exporting, setExporting] = useState<string | null>(null);
-  const [error, setError] = useState<string | null>(null);
-
-  useEffect(() => {
-    loadCheckpoints();
-  }, []);
-
-  const loadCheckpoints = async () => {
-    try {
-      const loaded = await invoke<Checkpoint[]>("list_checkpoints");
-      setCheckpoints(loaded);
-    } catch (err) {
-      // Mock data if command not implemented
-      setCheckpoints([
-        {
-          id: "ckpt-001",
-          model_id: "csi-encoder-cnn",
-          name: "CSI-CNN v1.2",
-          epoch: 50,
-          val_loss: 0.0234,
-          created_at: "2026-03-08T14:30:00Z",
-          path: "~/.ruview/models/csi-cnn-v1.2.pt",
-          size_mb: 12.4,
-        },
-        {
-          id: "ckpt-002",
-          model_id: "pose-decoder-gru",
-          name: "Pose-GRU v2.0",
-          epoch: 100,
-          val_loss: 0.0189,
-          created_at: "2026-03-09T09:15:00Z",
-          path: "~/.ruview/models/pose-gru-v2.pt",
-          size_mb: 8.2,
-        },
-      ]);
-    }
-  };
-
-  const handleExport = async (checkpointId: string, format: "onnx" | "torchscript") => {
-    setExporting(checkpointId);
-    setError(null);
-    try {
-      await invoke("export_model", { checkpointId, format });
-      // Success notification would go here
-    } catch (err) {
-      setError(`Export failed: ${err}`);
-    } finally {
-      setExporting(null);
-    }
-  };
-
-  const getTypeColor = (type: ModelArchitecture["type"]) => {
-    switch (type) {
-      case "encoder":
-        return "var(--accent)";
-      case "decoder":
-        return "var(--status-online)";
-      case "embedding":
-        return "#a855f7";
-      case "adaptor":
-        return "#f59e0b";
-    }
-  };
-
-  return (
-    <div>
-      {/* Stats Row */}
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(4, 1fr)",
-          gap: "var(--space-4)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        <StatCard label="Architectures" value={MODEL_ARCHITECTURES.length} />
-        <StatCard
-          label="Checkpoints"
-          value={checkpoints.length}
-          color="var(--status-online)"
-        />
-        <StatCard
-          label="Total Params"
-          value={`${MODEL_ARCHITECTURES.reduce((acc, m) => acc + m.params_m, 0).toFixed(1)}M`}
-        />
-        <StatCard
-          label="Storage Used"
-          value={`${checkpoints.reduce((acc, c) => acc + c.size_mb, 0).toFixed(1)} MB`}
-        />
-      </div>
-
-      {error && (
-        <div
-          style={{
-            background: "rgba(248, 81, 73, 0.1)",
-            border: "1px solid rgba(248, 81, 73, 0.3)",
-            borderRadius: 6,
-            padding: "var(--space-3)",
-            marginBottom: "var(--space-4)",
-            fontSize: 13,
-            color: "var(--status-error)",
-          }}
-        >
-          {error}
-        </div>
-      )}
-
-      {/* Model Architectures */}
-      <h3 style={{ fontSize: 14, fontWeight: 600, marginBottom: "var(--space-3)" }}>
-        Available Architectures
-      </h3>
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(auto-fill, minmax(300px, 1fr))",
-          gap: "var(--space-3)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        {MODEL_ARCHITECTURES.map((model) => (
-          <div
-            key={model.id}
-            className="card"
-            style={{
-              padding: "var(--space-3)",
-              cursor: "pointer",
-              border:
-                selectedModel === model.id
-                  ? "1px solid var(--accent)"
-                  : "1px solid transparent",
-            }}
-            onClick={() => setSelectedModel(model.id)}
-          >
-            <div
-              style={{
-                display: "flex",
-                justifyContent: "space-between",
-                alignItems: "start",
-                marginBottom: "var(--space-2)",
-              }}
-            >
-              <div>
-                <h4 style={{ margin: 0, fontSize: 14, fontWeight: 600 }}>
-                  {model.name}
-                </h4>
-                <span
-                  style={{
-                    display: "inline-block",
-                    marginTop: 4,
-                    padding: "1px 6px",
-                    borderRadius: 3,
-                    fontSize: 10,
-                    fontWeight: 600,
-                    textTransform: "uppercase",
-                    background: `${getTypeColor(model.type)}20`,
-                    color: getTypeColor(model.type),
-                  }}
-                >
-                  {model.type}
-                </span>
-              </div>
-              {model.paper && (
-                <span
-                  style={{
-                    fontSize: 10,
-                    color: "var(--text-muted)",
-                    fontStyle: "italic",
-                  }}
-                >
-                  {model.paper}
-                </span>
-              )}
-            </div>
-            <p
-              style={{
-                fontSize: 11,
-                color: "var(--text-muted)",
-                margin: "var(--space-2) 0",
-                lineHeight: 1.4,
-              }}
-            >
-              {model.description}
-            </p>
-            <div
-              style={{
-                display: "flex",
-                gap: "var(--space-3)",
-                fontSize: 11,
-                color: "var(--text-secondary)",
-              }}
-            >
-              <span>🧮 {model.params_m}M params</span>
-              <span>💾 {model.memory_mb} MB</span>
-            </div>
-          </div>
-        ))}
-      </div>
-
-      {/* Checkpoints */}
-      <h3 style={{ fontSize: 14, fontWeight: 600, marginBottom: "var(--space-3)" }}>
-        Saved Checkpoints
-      </h3>
-      {checkpoints.length === 0 ? (
-        <div
-          className="card"
-          style={{
-            padding: "var(--space-5)",
-            textAlign: "center",
-            color: "var(--text-muted)",
-          }}
-        >
-          <div style={{ fontSize: 32, marginBottom: "var(--space-2)" }}>📦</div>
-          <p style={{ fontSize: 13 }}>No checkpoints saved yet</p>
-          <p style={{ fontSize: 12 }}>Train a model to create checkpoints</p>
-        </div>
-      ) : (
-        <div style={{ display: "flex", flexDirection: "column", gap: "var(--space-2)" }}>
-          {checkpoints.map((ckpt) => (
-            <div
-              key={ckpt.id}
-              className="card"
-              style={{
-                padding: "var(--space-3)",
-                display: "flex",
-                justifyContent: "space-between",
-                alignItems: "center",
-              }}
-            >
-              <div>
-                <div style={{ fontWeight: 600, fontSize: 13 }}>{ckpt.name}</div>
-                <div
-                  style={{
-                    fontSize: 11,
-                    color: "var(--text-muted)",
-                    marginTop: 2,
-                  }}
-                >
-                  Epoch {ckpt.epoch} • Val Loss: {ckpt.val_loss.toFixed(4)} •{" "}
-                  {ckpt.size_mb.toFixed(1)} MB
-                </div>
-              </div>
-              <div style={{ display: "flex", gap: "var(--space-2)" }}>
-                <button
-                  onClick={() => handleExport(ckpt.id, "onnx")}
-                  disabled={exporting === ckpt.id}
-                  style={{
-                    padding: "6px 12px",
-                    background: "rgba(56, 139, 253, 0.1)",
-                    border: "1px solid rgba(56, 139, 253, 0.3)",
-                    borderRadius: 4,
-                    color: "var(--accent)",
-                    fontSize: 11,
-                    fontWeight: 600,
-                    cursor: exporting === ckpt.id ? "wait" : "pointer",
-                    opacity: exporting === ckpt.id ? 0.6 : 1,
-                  }}
-                >
-                  {exporting === ckpt.id ? "Exporting..." : "ONNX"}
-                </button>
-                <button
-                  onClick={() => handleExport(ckpt.id, "torchscript")}
-                  disabled={exporting === ckpt.id}
-                  style={{
-                    padding: "6px 12px",
-                    background: "transparent",
-                    border: "1px solid var(--border)",
-                    borderRadius: 4,
-                    color: "var(--text-secondary)",
-                    fontSize: 11,
-                    fontWeight: 600,
-                    cursor: exporting === ckpt.id ? "wait" : "pointer",
-                    opacity: exporting === ckpt.id ? 0.6 : 1,
-                  }}
-                >
-                  TorchScript
-                </button>
-              </div>
-            </div>
-          ))}
-        </div>
-      )}
-    </div>
-  );
-};
-
-function StatCard({
-  label,
-  value,
-  color,
-}: {
-  label: string;
-  value: number | string;
-  color?: string;
-}) {
-  return (
-    <div className="card-glow" style={{ padding: "var(--space-4)" }}>
-      <div
-        style={{
-          fontSize: 10,
-          textTransform: "uppercase",
-          letterSpacing: "0.06em",
-          color: "var(--text-muted)",
-          marginBottom: "var(--space-2)",
-          fontWeight: 600,
-        }}
-      >
-        {label}
-      </div>
-      <div
-        style={{
-          fontFamily: "var(--font-mono)",
-          fontSize: 28,
-          fontWeight: 600,
-          color: color || "var(--text-primary)",
-          letterSpacing: "-0.02em",
-        }}
-      >
-        {value}
-      </div>
-    </div>
-  );
-}
-
-export default ModelsTab;
@@ -1,767 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-
-interface RuVectorConfig {
-  // MinCut Parameters
-  mincut_enabled: boolean;
-  mincut_threshold: number;
-  mincut_max_persons: number;
-
-  // Attention Parameters
-  attention_enabled: boolean;
-  attention_heads: number;
-  attention_dropout: number;
-
-  // Temporal Parameters
-  temporal_enabled: boolean;
-  temporal_window_ms: number;
-  temporal_compression_ratio: number;
-
-  // Solver Parameters
-  solver_enabled: boolean;
-  solver_interpolation: "linear" | "cubic" | "sparse";
-  solver_subcarrier_count: number;
-
-  // BVP Parameters
-  bvp_enabled: boolean;
-  bvp_filter_hz: [number, number];
-}
-
-const DEFAULT_CONFIG: RuVectorConfig = {
-  mincut_enabled: true,
-  mincut_threshold: 0.5,
-  mincut_max_persons: 5,
-  attention_enabled: true,
-  attention_heads: 4,
-  attention_dropout: 0.1,
-  temporal_enabled: true,
-  temporal_window_ms: 500,
-  temporal_compression_ratio: 4,
-  solver_enabled: true,
-  solver_interpolation: "sparse",
-  solver_subcarrier_count: 56,
-  bvp_enabled: false,
-  bvp_filter_hz: [0.7, 4.0],
-};
-
-const MODULES = [
-  {
-    id: "mincut",
-    name: "MinCut Segmentation",
-    crate: "ruvector-mincut",
-    description: "Graph-based person segmentation using DynamicPersonMatcher",
-    icon: "✂️",
-  },
-  {
-    id: "attention",
-    name: "Spatial Attention",
-    crate: "ruvector-attention",
-    description: "Attention-weighted antenna selection and BVP extraction",
-    icon: "🎯",
-  },
-  {
-    id: "temporal",
-    name: "Temporal Tensor",
-    crate: "ruvector-temporal-tensor",
-    description: "Temporal CSI compression and breathing detection",
-    icon: "⏱️",
-  },
-  {
-    id: "solver",
-    name: "Sparse Solver",
-    crate: "ruvector-solver",
-    description: "Sparse interpolation (114→56 subcarriers) and triangulation",
-    icon: "🧮",
-  },
-  {
-    id: "attn-mincut",
-    name: "Attention MinCut",
-    crate: "ruvector-attn-mincut",
-    description: "Combined attention-weighted graph segmentation",
-    icon: "🔀",
-  },
-];
-
-const RuVectorTab: React.FC = () => {
-  const [config, setConfig] = useState<RuVectorConfig>(DEFAULT_CONFIG);
-  const [testingLive, setTestingLive] = useState(false);
-  const [liveMetrics, setLiveMetrics] = useState<{
-    fps: number;
-    latency_ms: number;
-    persons_detected: number;
-  } | null>(null);
-  const [saved, setSaved] = useState(true);
-  const [error, setError] = useState<string | null>(null);
-
-  useEffect(() => {
-    loadConfig();
-  }, []);
-
-  const loadConfig = async () => {
-    try {
-      const loaded = await invoke<RuVectorConfig>("get_ruvector_config");
-      setConfig(loaded);
-    } catch (err) {
-      // Use defaults if not implemented
-    }
-  };
-
-  const saveConfig = async () => {
-    setError(null);
-    try {
-      await invoke("set_ruvector_config", { config });
-      setSaved(true);
-    } catch (err) {
-      setError(`Failed to save: ${err}`);
-    }
-  };
-
-  const handleChange = <K extends keyof RuVectorConfig>(
-    key: K,
-    value: RuVectorConfig[K]
-  ) => {
-    setConfig((prev) => ({ ...prev, [key]: value }));
-    setSaved(false);
-  };
-
-  const startLiveTest = async () => {
-    setTestingLive(true);
-    setError(null);
-    try {
-      // Simulate live testing metrics
-      const interval = setInterval(() => {
-        setLiveMetrics({
-          fps: 25 + Math.random() * 10,
-          latency_ms: 15 + Math.random() * 10,
-          persons_detected: Math.floor(Math.random() * 3) + 1,
-        });
-      }, 500);
-
-      // Stop after 10 seconds for demo
-      setTimeout(() => {
-        clearInterval(interval);
-        setTestingLive(false);
-        setLiveMetrics(null);
-      }, 10000);
-    } catch (err) {
-      setError(`Live test failed: ${err}`);
-      setTestingLive(false);
-    }
-  };
-
-  const exportConfig = () => {
-    const blob = new Blob([JSON.stringify(config, null, 2)], {
-      type: "application/json",
-    });
-    const url = URL.createObjectURL(blob);
-    const a = document.createElement("a");
-    a.href = url;
-    a.download = "ruvector-config.json";
-    a.click();
-    URL.revokeObjectURL(url);
-  };
-
-  return (
-    <div>
-      {/* Module Cards */}
-      <div
-        style={{
-          display: "grid",
-          gridTemplateColumns: "repeat(auto-fill, minmax(200px, 1fr))",
-          gap: "var(--space-3)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        {MODULES.map((mod) => {
-          const isEnabled =
-            config[`${mod.id.replace("-", "_")}_enabled` as keyof RuVectorConfig] ?? true;
-          return (
-            <div
-              key={mod.id}
-              className="card"
-              style={{
-                padding: "var(--space-3)",
-                opacity: isEnabled ? 1 : 0.5,
-                transition: "opacity 0.2s",
-              }}
-            >
-              <div
-                style={{
-                  display: "flex",
-                  justifyContent: "space-between",
-                  alignItems: "start",
-                }}
-              >
-                <span style={{ fontSize: 24 }}>{mod.icon}</span>
-                <span
-                  style={{
-                    fontSize: 9,
-                    padding: "2px 6px",
-                    borderRadius: 3,
-                    background: isEnabled
-                      ? "rgba(63, 185, 80, 0.15)"
-                      : "rgba(139, 148, 158, 0.15)",
-                    color: isEnabled ? "var(--status-online)" : "var(--text-muted)",
-                    fontWeight: 600,
-                  }}
-                >
-                  {isEnabled ? "ON" : "OFF"}
-                </span>
-              </div>
-              <h4 style={{ margin: "var(--space-2) 0 4px", fontSize: 13, fontWeight: 600 }}>
-                {mod.name}
-              </h4>
-              <p
-                style={{
-                  fontSize: 11,
-                  color: "var(--text-muted)",
-                  margin: 0,
-                  lineHeight: 1.4,
-                }}
-              >
-                {mod.description}
-              </p>
-              <div
-                style={{
-                  marginTop: "var(--space-2)",
-                  fontFamily: "var(--font-mono)",
-                  fontSize: 10,
-                  color: "var(--text-secondary)",
-                }}
-              >
-                {mod.crate}
-              </div>
-            </div>
-          );
-        })}
-      </div>
-
-      {error && (
-        <div
-          style={{
-            background: "rgba(248, 81, 73, 0.1)",
-            border: "1px solid rgba(248, 81, 73, 0.3)",
-            borderRadius: 6,
-            padding: "var(--space-3)",
-            marginBottom: "var(--space-4)",
-            fontSize: 13,
-            color: "var(--status-error)",
-          }}
-        >
-          {error}
-        </div>
-      )}
-
-      <div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: "var(--space-5)" }}>
-        {/* Configuration Panel */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-            Module Configuration
-          </h3>
-
-          {/* MinCut Section */}
-          <ConfigSection title="MinCut Segmentation">
-            <ToggleRow
-              label="Enable MinCut"
-              checked={config.mincut_enabled}
-              onChange={(v) => handleChange("mincut_enabled", v)}
-            />
-            <SliderRow
-              label="Threshold"
-              value={config.mincut_threshold}
-              min={0.1}
-              max={1.0}
-              step={0.05}
-              onChange={(v) => handleChange("mincut_threshold", v)}
-              disabled={!config.mincut_enabled}
-            />
-            <NumberRow
-              label="Max Persons"
-              value={config.mincut_max_persons}
-              min={1}
-              max={10}
-              onChange={(v) => handleChange("mincut_max_persons", v)}
-              disabled={!config.mincut_enabled}
-            />
-          </ConfigSection>
-
-          {/* Attention Section */}
-          <ConfigSection title="Spatial Attention">
-            <ToggleRow
-              label="Enable Attention"
-              checked={config.attention_enabled}
-              onChange={(v) => handleChange("attention_enabled", v)}
-            />
-            <NumberRow
-              label="Attention Heads"
-              value={config.attention_heads}
-              min={1}
-              max={16}
-              onChange={(v) => handleChange("attention_heads", v)}
-              disabled={!config.attention_enabled}
-            />
-            <SliderRow
-              label="Dropout"
-              value={config.attention_dropout}
-              min={0}
-              max={0.5}
-              step={0.05}
-              onChange={(v) => handleChange("attention_dropout", v)}
-              disabled={!config.attention_enabled}
-            />
-          </ConfigSection>
-
-          {/* Temporal Section */}
-          <ConfigSection title="Temporal Processing">
-            <ToggleRow
-              label="Enable Temporal"
-              checked={config.temporal_enabled}
-              onChange={(v) => handleChange("temporal_enabled", v)}
-            />
-            <NumberRow
-              label="Window (ms)"
-              value={config.temporal_window_ms}
-              min={100}
-              max={2000}
-              step={100}
-              onChange={(v) => handleChange("temporal_window_ms", v)}
-              disabled={!config.temporal_enabled}
-            />
-            <NumberRow
-              label="Compression Ratio"
-              value={config.temporal_compression_ratio}
-              min={1}
-              max={16}
-              onChange={(v) => handleChange("temporal_compression_ratio", v)}
-              disabled={!config.temporal_enabled}
-            />
-          </ConfigSection>
-
-          {/* Solver Section */}
-          <ConfigSection title="Sparse Solver">
-            <ToggleRow
-              label="Enable Solver"
-              checked={config.solver_enabled}
-              onChange={(v) => handleChange("solver_enabled", v)}
-            />
-            <div style={{ marginBottom: "var(--space-2)" }}>
-              <label style={labelStyle}>Interpolation</label>
-              <select
-                value={config.solver_interpolation}
-                onChange={(e) =>
-                  handleChange(
-                    "solver_interpolation",
-                    e.target.value as RuVectorConfig["solver_interpolation"]
-                  )
-                }
-                disabled={!config.solver_enabled}
-                style={{
-                  ...inputStyle,
-                  opacity: config.solver_enabled ? 1 : 0.5,
-                }}
-              >
-                <option value="linear">Linear</option>
-                <option value="cubic">Cubic</option>
-                <option value="sparse">Sparse (L1)</option>
-              </select>
-            </div>
-            <NumberRow
-              label="Subcarrier Count"
-              value={config.solver_subcarrier_count}
-              min={28}
-              max={114}
-              onChange={(v) => handleChange("solver_subcarrier_count", v)}
-              disabled={!config.solver_enabled}
-            />
-          </ConfigSection>
-
-          {/* Action Buttons */}
-          <div
-            style={{
-              display: "flex",
-              gap: "var(--space-2)",
-              marginTop: "var(--space-4)",
-            }}
-          >
-            <button
-              onClick={saveConfig}
-              className="btn-gradient"
-              style={{
-                flex: 1,
-                padding: "10px",
-                fontSize: 12,
-                opacity: saved ? 0.6 : 1,
-              }}
-              disabled={saved}
-            >
-              {saved ? "Saved" : "Save Configuration"}
-            </button>
-            <button
-              onClick={exportConfig}
-              style={{
-                padding: "10px 16px",
-                background: "transparent",
-                border: "1px solid var(--border)",
-                borderRadius: 6,
-                color: "var(--text-secondary)",
-                fontSize: 12,
-                fontWeight: 600,
-                cursor: "pointer",
-              }}
-            >
-              Export
-            </button>
-          </div>
-        </div>
-
-        {/* Live Testing Panel */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-            Live Testing
-          </h3>
-
-          <div
-            style={{
-              display: "flex",
-              flexDirection: "column",
-              alignItems: "center",
-              justifyContent: "center",
-              minHeight: 200,
-              background: "var(--bg-secondary)",
-              borderRadius: 8,
-              marginBottom: "var(--space-4)",
-            }}
-          >
-            {testingLive ? (
-              <>
-                <div
-                  style={{
-                    fontSize: 48,
-                    animation: "pulse 1s infinite",
-                  }}
-                >
-                  📡
-                </div>
-                <p style={{ fontSize: 13, color: "var(--text-secondary)", marginTop: "var(--space-2)" }}>
-                  Processing live CSI stream...
-                </p>
-              </>
-            ) : (
-              <>
-                <div style={{ fontSize: 48, opacity: 0.5 }}>📡</div>
-                <p style={{ fontSize: 13, color: "var(--text-muted)", marginTop: "var(--space-2)" }}>
-                  Start live test to apply config to real CSI data
-                </p>
-              </>
-            )}
-          </div>
-
-          {liveMetrics && (
-            <div
-              style={{
-                display: "grid",
-                gridTemplateColumns: "repeat(3, 1fr)",
-                gap: "var(--space-3)",
-                marginBottom: "var(--space-4)",
-              }}
-            >
-              <MetricCard label="FPS" value={liveMetrics.fps.toFixed(1)} />
-              <MetricCard label="Latency" value={`${liveMetrics.latency_ms.toFixed(0)}ms`} />
-              <MetricCard label="Persons" value={liveMetrics.persons_detected.toString()} />
-            </div>
-          )}
-
-          <button
-            onClick={testingLive ? () => setTestingLive(false) : startLiveTest}
-            style={{
-              width: "100%",
-              padding: "12px",
-              background: testingLive
-                ? "rgba(248, 81, 73, 0.1)"
-                : "rgba(56, 139, 253, 0.1)",
-              border: `1px solid ${testingLive ? "rgba(248, 81, 73, 0.3)" : "rgba(56, 139, 253, 0.3)"}`,
-              borderRadius: 6,
-              color: testingLive ? "var(--status-error)" : "var(--accent)",
-              fontSize: 13,
-              fontWeight: 600,
-              cursor: "pointer",
-            }}
-          >
-            {testingLive ? "Stop Test" : "Start Live Test"}
-          </button>
-
-          {/* Presets */}
-          <div style={{ marginTop: "var(--space-5)" }}>
-            <h4 style={{ fontSize: 12, fontWeight: 600, marginBottom: "var(--space-3)" }}>
-              Quick Presets
-            </h4>
-            <div style={{ display: "flex", flexDirection: "column", gap: "var(--space-2)" }}>
-              <PresetButton
-                label="Low Latency"
-                description="Minimal processing for real-time"
-                onClick={() => {
-                  setConfig({
-                    ...DEFAULT_CONFIG,
-                    attention_heads: 2,
-                    temporal_compression_ratio: 8,
-                    solver_subcarrier_count: 28,
-                  });
-                  setSaved(false);
-                }}
-              />
-              <PresetButton
-                label="High Accuracy"
-                description="Maximum quality, higher latency"
-                onClick={() => {
-                  setConfig({
-                    ...DEFAULT_CONFIG,
-                    attention_heads: 8,
-                    temporal_compression_ratio: 2,
-                    solver_subcarrier_count: 114,
-                    solver_interpolation: "cubic",
-                  });
-                  setSaved(false);
-                }}
-              />
-              <PresetButton
-                label="Balanced"
-                description="Default recommended settings"
-                onClick={() => {
-                  setConfig(DEFAULT_CONFIG);
-                  setSaved(false);
-                }}
-              />
-            </div>
-          </div>
-        </div>
-      </div>
-
-      <style>{`
-        @keyframes pulse {
-          0%, 100% { transform: scale(1); }
-          50% { transform: scale(1.1); }
-        }
-      `}</style>
-    </div>
-  );
-};
-
-// Helper Components
-function ConfigSection({ title, children }: { title: string; children: React.ReactNode }) {
-  return (
-    <div style={{ marginBottom: "var(--space-4)" }}>
-      <h4
-        style={{
-          fontSize: 11,
-          fontWeight: 600,
-          color: "var(--text-muted)",
-          textTransform: "uppercase",
-          letterSpacing: "0.04em",
-          marginBottom: "var(--space-2)",
-        }}
-      >
-        {title}
-      </h4>
-      {children}
-    </div>
-  );
-}
-
-function ToggleRow({
-  label,
-  checked,
-  onChange,
-}: {
-  label: string;
-  checked: boolean;
-  onChange: (v: boolean) => void;
-}) {
-  return (
-    <div
-      style={{
-        display: "flex",
-        justifyContent: "space-between",
-        alignItems: "center",
-        marginBottom: "var(--space-2)",
-      }}
-    >
-      <span style={{ fontSize: 12 }}>{label}</span>
-      <button
-        onClick={() => onChange(!checked)}
-        style={{
-          width: 40,
-          height: 22,
-          borderRadius: 11,
-          border: "none",
-          background: checked ? "var(--accent)" : "var(--border)",
-          position: "relative",
-          cursor: "pointer",
-          transition: "background 0.2s",
-        }}
-      >
-        <span
-          style={{
-            position: "absolute",
-            top: 2,
-            left: checked ? 20 : 2,
-            width: 18,
-            height: 18,
-            borderRadius: "50%",
-            background: "white",
-            transition: "left 0.2s",
-          }}
-        />
-      </button>
-    </div>
-  );
-}
-
-function SliderRow({
-  label,
-  value,
-  min,
-  max,
-  step,
-  onChange,
-  disabled,
-}: {
-  label: string;
-  value: number;
-  min: number;
-  max: number;
-  step: number;
-  onChange: (v: number) => void;
-  disabled?: boolean;
-}) {
-  return (
-    <div style={{ marginBottom: "var(--space-2)", opacity: disabled ? 0.5 : 1 }}>
-      <div style={{ display: "flex", justifyContent: "space-between", marginBottom: 4 }}>
-        <span style={{ fontSize: 12 }}>{label}</span>
-        <span style={{ fontSize: 11, fontFamily: "var(--font-mono)", color: "var(--text-muted)" }}>
-          {value.toFixed(2)}
-        </span>
-      </div>
-      <input
-        type="range"
-        value={value}
-        min={min}
-        max={max}
-        step={step}
-        onChange={(e) => onChange(parseFloat(e.target.value))}
-        disabled={disabled}
-        style={{ width: "100%", cursor: disabled ? "not-allowed" : "pointer" }}
-      />
-    </div>
-  );
-}
-
-function NumberRow({
-  label,
-  value,
-  min,
-  max,
-  step = 1,
-  onChange,
-  disabled,
-}: {
-  label: string;
-  value: number;
-  min: number;
-  max: number;
-  step?: number;
-  onChange: (v: number) => void;
-  disabled?: boolean;
-}) {
-  return (
-    <div
-      style={{
-        display: "flex",
-        justifyContent: "space-between",
-        alignItems: "center",
-        marginBottom: "var(--space-2)",
-        opacity: disabled ? 0.5 : 1,
-      }}
-    >
-      <span style={{ fontSize: 12 }}>{label}</span>
-      <input
-        type="number"
-        value={value}
-        min={min}
-        max={max}
-        step={step}
-        onChange={(e) => onChange(parseInt(e.target.value) || min)}
-        disabled={disabled}
-        style={{
-          width: 70,
-          padding: "4px 8px",
-          background: "var(--bg-secondary)",
-          border: "1px solid var(--border)",
-          borderRadius: 4,
-          color: "var(--text-primary)",
-          fontSize: 12,
-          textAlign: "right",
-          cursor: disabled ? "not-allowed" : "text",
-        }}
-      />
-    </div>
-  );
-}
-
-function MetricCard({ label, value }: { label: string; value: string }) {
-  return (
-    <div className="card" style={{ padding: "var(--space-3)", textAlign: "center" }}>
-      <div style={{ fontSize: 10, color: "var(--text-muted)", marginBottom: 2 }}>{label}</div>
-      <div style={{ fontFamily: "var(--font-mono)", fontSize: 18, fontWeight: 600 }}>{value}</div>
-    </div>
-  );
-}
-
-function PresetButton({
-  label,
-  description,
-  onClick,
-}: {
-  label: string;
-  description: string;
-  onClick: () => void;
-}) {
-  return (
-    <button
-      onClick={onClick}
-      style={{
-        display: "flex",
-        flexDirection: "column",
-        alignItems: "start",
-        padding: "var(--space-3)",
-        background: "var(--bg-secondary)",
-        border: "1px solid var(--border)",
-        borderRadius: 6,
-        cursor: "pointer",
-        textAlign: "left",
-      }}
-    >
-      <span style={{ fontSize: 12, fontWeight: 600, color: "var(--text-primary)" }}>{label}</span>
-      <span style={{ fontSize: 11, color: "var(--text-muted)" }}>{description}</span>
-    </button>
-  );
-}
-
-const labelStyle: React.CSSProperties = {
-  display: "block",
-  fontSize: 11,
-  fontWeight: 600,
-  color: "var(--text-muted)",
-  marginBottom: 4,
-};
-
-const inputStyle: React.CSSProperties = {
-  width: "100%",
-  padding: "8px 12px",
-  background: "var(--bg-secondary)",
-  border: "1px solid var(--border)",
-  borderRadius: 6,
-  color: "var(--text-primary)",
-  fontSize: 13,
-};
-
-export default RuVectorTab;
@@ -1,601 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-import { listen, UnlistenFn } from "@tauri-apps/api/event";
-
-interface TrainingConfig {
-  dataset_id: string;
-  model_id: string;
-  epochs: number;
-  batch_size: number;
-  learning_rate: number;
-  optimizer: "adam" | "sgd" | "adamw";
-  weight_decay: number;
-  use_augmentation: boolean;
-  checkpoint_every: number;
-}
-
-interface TrainingProgress {
-  epoch: number;
-  total_epochs: number;
-  batch: number;
-  total_batches: number;
-  train_loss: number;
-  val_loss: number | null;
-  learning_rate: number;
-  eta_secs: number;
-  gpu_memory_mb: number | null;
-}
-
-interface TrainingJob {
-  id: string;
-  status: "running" | "paused" | "completed" | "failed";
-  started_at: string;
-  progress: TrainingProgress;
-}
-
-const DEFAULT_CONFIG: TrainingConfig = {
-  dataset_id: "mmfi",
-  model_id: "csi-encoder-cnn",
-  epochs: 100,
-  batch_size: 32,
-  learning_rate: 0.001,
-  optimizer: "adam",
-  weight_decay: 0.0001,
-  use_augmentation: true,
-  checkpoint_every: 10,
-};
-
-interface TrainingTabProps {
-  gpuAvailable: boolean;
-}
-
-const TrainingTab: React.FC<TrainingTabProps> = ({ gpuAvailable }) => {
-  const [config, setConfig] = useState<TrainingConfig>(DEFAULT_CONFIG);
-  const [currentJob, setCurrentJob] = useState<TrainingJob | null>(null);
-  const [lossHistory, setLossHistory] = useState<{ epoch: number; train: number; val: number }[]>(
-    []
-  );
-  const [error, setError] = useState<string | null>(null);
-
-  useEffect(() => {
-    let unlisten: UnlistenFn | undefined;
-
-    const setupListener = async () => {
-      try {
-        unlisten = await listen<TrainingProgress>("training:progress", (event) => {
-          const progress = event.payload;
-          setCurrentJob((prev) =>
-            prev
-              ? { ...prev, progress }
-              : {
-                  id: "job-1",
-                  status: "running",
-                  started_at: new Date().toISOString(),
-                  progress,
-                }
-          );
-
-          if (progress.val_loss !== null && progress.batch === progress.total_batches) {
-            setLossHistory((prev) => [
-              ...prev,
-              {
-                epoch: progress.epoch,
-                train: progress.train_loss,
-                val: progress.val_loss!,
-              },
-            ]);
-          }
-        });
-      } catch (err) {
-        console.error("Failed to setup training listener:", err);
-      }
-    };
-
-    setupListener();
-
-    return () => {
-      if (unlisten) unlisten();
-    };
-  }, []);
-
-  const handleStartTraining = async () => {
-    setError(null);
-    try {
-      await invoke("start_training", { config });
-      setCurrentJob({
-        id: `job-${Date.now()}`,
-        status: "running",
-        started_at: new Date().toISOString(),
-        progress: {
-          epoch: 0,
-          total_epochs: config.epochs,
-          batch: 0,
-          total_batches: 0,
-          train_loss: 0,
-          val_loss: null,
-          learning_rate: config.learning_rate,
-          eta_secs: 0,
-          gpu_memory_mb: null,
-        },
-      });
-    } catch (err) {
-      setError(`Failed to start training: ${err}`);
-    }
-  };
-
-  const handleStopTraining = async () => {
-    try {
-      await invoke("stop_training");
-      setCurrentJob((prev) => (prev ? { ...prev, status: "paused" } : null));
-    } catch (err) {
-      setError(`Failed to stop training: ${err}`);
-    }
-  };
-
-  const formatEta = (seconds: number) => {
-    if (seconds < 60) return `${seconds}s`;
-    if (seconds < 3600) return `${Math.floor(seconds / 60)}m ${seconds % 60}s`;
-    const hours = Math.floor(seconds / 3600);
-    const mins = Math.floor((seconds % 3600) / 60);
-    return `${hours}h ${mins}m`;
-  };
-
-  const progress = currentJob?.progress;
-  const epochProgress = progress ? (progress.epoch / progress.total_epochs) * 100 : 0;
-  const batchProgress = progress && progress.total_batches > 0
-    ? (progress.batch / progress.total_batches) * 100
-    : 0;
-
-  return (
-    <div>
-      {/* GPU Warning */}
-      {!gpuAvailable && (
-        <div
-          style={{
-            background: "rgba(245, 158, 11, 0.1)",
-            border: "1px solid rgba(245, 158, 11, 0.3)",
-            borderRadius: 6,
-            padding: "var(--space-3)",
-            marginBottom: "var(--space-4)",
-            display: "flex",
-            alignItems: "center",
-            gap: "var(--space-3)",
-          }}
-        >
-          <span style={{ fontSize: 18 }}>⚠️</span>
-          <div>
-            <div style={{ fontWeight: 600, fontSize: 13, color: "#f59e0b" }}>
-              GPU Not Available
-            </div>
-            <div style={{ fontSize: 12, color: "var(--text-muted)" }}>
-              Training will use CPU, which is significantly slower. Consider using a
-              machine with CUDA or Metal support.
-            </div>
-          </div>
-        </div>
-      )}
-
-      {error && (
-        <div
-          style={{
-            background: "rgba(248, 81, 73, 0.1)",
-            border: "1px solid rgba(248, 81, 73, 0.3)",
-            borderRadius: 6,
-            padding: "var(--space-3)",
-            marginBottom: "var(--space-4)",
-            fontSize: 13,
-            color: "var(--status-error)",
-          }}
-        >
-          {error}
-        </div>
-      )}
-
-      <div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: "var(--space-5)" }}>
-        {/* Configuration Panel */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-            Training Configuration
-          </h3>
-
-          <div style={{ display: "flex", flexDirection: "column", gap: "var(--space-3)" }}>
-            <div>
-              <label style={labelStyle}>Dataset</label>
-              <select
-                value={config.dataset_id}
-                onChange={(e) => setConfig({ ...config, dataset_id: e.target.value })}
-                style={inputStyle}
-              >
-                <option value="mmfi">MM-Fi Dataset</option>
-                <option value="wipose">Wi-Pose Dataset</option>
-                <option value="wiar">WiAR Dataset</option>
-              </select>
-            </div>
-
-            <div>
-              <label style={labelStyle}>Model Architecture</label>
-              <select
-                value={config.model_id}
-                onChange={(e) => setConfig({ ...config, model_id: e.target.value })}
-                style={inputStyle}
-              >
-                <option value="csi-encoder-cnn">CSI Encoder (CNN)</option>
-                <option value="csi-encoder-transformer">CSI Encoder (Transformer)</option>
-                <option value="pose-decoder-lstm">Pose Decoder (LSTM)</option>
-                <option value="pose-decoder-gru">Pose Decoder (GRU)</option>
-              </select>
-            </div>
-
-            <div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: "var(--space-3)" }}>
-              <div>
-                <label style={labelStyle}>Epochs</label>
-                <input
-                  type="number"
-                  value={config.epochs}
-                  onChange={(e) => setConfig({ ...config, epochs: parseInt(e.target.value) || 1 })}
-                  min={1}
-                  max={1000}
-                  style={inputStyle}
-                />
-              </div>
-              <div>
-                <label style={labelStyle}>Batch Size</label>
-                <input
-                  type="number"
-                  value={config.batch_size}
-                  onChange={(e) =>
-                    setConfig({ ...config, batch_size: parseInt(e.target.value) || 1 })
-                  }
-                  min={1}
-                  max={512}
-                  style={inputStyle}
-                />
-              </div>
-            </div>
-
-            <div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: "var(--space-3)" }}>
-              <div>
-                <label style={labelStyle}>Learning Rate</label>
-                <input
-                  type="number"
-                  value={config.learning_rate}
-                  onChange={(e) =>
-                    setConfig({ ...config, learning_rate: parseFloat(e.target.value) || 0.001 })
-                  }
-                  step={0.0001}
-                  min={0.00001}
-                  max={1}
-                  style={inputStyle}
-                />
-              </div>
-              <div>
-                <label style={labelStyle}>Optimizer</label>
-                <select
-                  value={config.optimizer}
-                  onChange={(e) =>
-                    setConfig({ ...config, optimizer: e.target.value as TrainingConfig["optimizer"] })
-                  }
-                  style={inputStyle}
-                >
-                  <option value="adam">Adam</option>
-                  <option value="adamw">AdamW</option>
-                  <option value="sgd">SGD</option>
-                </select>
-              </div>
-            </div>
-
-            <div style={{ display: "grid", gridTemplateColumns: "1fr 1fr", gap: "var(--space-3)" }}>
-              <div>
-                <label style={labelStyle}>Weight Decay</label>
-                <input
-                  type="number"
-                  value={config.weight_decay}
-                  onChange={(e) =>
-                    setConfig({ ...config, weight_decay: parseFloat(e.target.value) || 0 })
-                  }
-                  step={0.0001}
-                  min={0}
-                  max={1}
-                  style={inputStyle}
-                />
-              </div>
-              <div>
-                <label style={labelStyle}>Checkpoint Every</label>
-                <input
-                  type="number"
-                  value={config.checkpoint_every}
-                  onChange={(e) =>
-                    setConfig({ ...config, checkpoint_every: parseInt(e.target.value) || 1 })
-                  }
-                  min={1}
-                  max={100}
-                  style={inputStyle}
-                />
-              </div>
-            </div>
-
-            <div style={{ display: "flex", alignItems: "center", gap: "var(--space-2)" }}>
-              <input
-                type="checkbox"
-                id="augmentation"
-                checked={config.use_augmentation}
-                onChange={(e) => setConfig({ ...config, use_augmentation: e.target.checked })}
-                style={{ width: 16, height: 16 }}
-              />
-              <label htmlFor="augmentation" style={{ fontSize: 13, cursor: "pointer" }}>
-                Enable Data Augmentation
-              </label>
-            </div>
-
-            <div style={{ marginTop: "var(--space-3)" }}>
-              {currentJob?.status === "running" ? (
-                <button
-                  onClick={handleStopTraining}
-                  style={{
-                    width: "100%",
-                    padding: "12px",
-                    background: "rgba(248, 81, 73, 0.1)",
-                    border: "1px solid rgba(248, 81, 73, 0.3)",
-                    borderRadius: 6,
-                    color: "var(--status-error)",
-                    fontSize: 13,
-                    fontWeight: 600,
-                    cursor: "pointer",
-                  }}
-                >
-                  Stop Training
-                </button>
-              ) : (
-                <button
-                  onClick={handleStartTraining}
-                  className="btn-gradient"
-                  style={{ width: "100%", padding: "12px", fontSize: 13 }}
-                >
-                  Start Training
-                </button>
-              )}
-            </div>
-          </div>
-        </div>
-
-        {/* Progress Panel */}
-        <div className="card" style={{ padding: "var(--space-4)" }}>
-          <h3 style={{ margin: 0, fontSize: 14, fontWeight: 600, marginBottom: "var(--space-4)" }}>
-            Training Progress
-          </h3>
-
-          {!currentJob ? (
-            <div
-              style={{
-                display: "flex",
-                flexDirection: "column",
-                alignItems: "center",
-                justifyContent: "center",
-                height: 300,
-                color: "var(--text-muted)",
-              }}
-            >
-              <div style={{ fontSize: 48, marginBottom: "var(--space-3)" }}>🎯</div>
-              <p style={{ fontSize: 13 }}>No training job running</p>
-              <p style={{ fontSize: 12 }}>Configure and start training to begin</p>
-            </div>
-          ) : (
-            <div style={{ display: "flex", flexDirection: "column", gap: "var(--space-4)" }}>
-              {/* Status */}
-              <div
-                style={{
-                  display: "flex",
-                  justifyContent: "space-between",
-                  alignItems: "center",
-                }}
-              >
-                <div style={{ display: "flex", alignItems: "center", gap: "var(--space-2)" }}>
-                  <span
-                    style={{
-                      width: 8,
-                      height: 8,
-                      borderRadius: "50%",
-                      background:
-                        currentJob.status === "running"
-                          ? "var(--status-online)"
-                          : currentJob.status === "paused"
-                            ? "#f59e0b"
-                            : "var(--status-error)",
-                      animation: currentJob.status === "running" ? "pulse 1.5s infinite" : "none",
-                    }}
-                  />
-                  <span style={{ fontSize: 13, fontWeight: 600, textTransform: "capitalize" }}>
-                    {currentJob.status}
-                  </span>
-                </div>
-                <span style={{ fontSize: 12, color: "var(--text-muted)" }}>
-                  ETA: {formatEta(progress?.eta_secs ?? 0)}
-                </span>
-              </div>
-
-              {/* Epoch Progress */}
-              <div>
-                <div
-                  style={{
-                    display: "flex",
-                    justifyContent: "space-between",
-                    fontSize: 12,
-                    marginBottom: 4,
-                  }}
-                >
-                  <span>Epoch</span>
-                  <span>
-                    {progress?.epoch ?? 0} / {progress?.total_epochs ?? config.epochs}
-                  </span>
-                </div>
-                <div
-                  style={{
-                    height: 6,
-                    background: "var(--border)",
-                    borderRadius: 3,
-                    overflow: "hidden",
-                  }}
-                >
-                  <div
-                    style={{
-                      width: `${epochProgress}%`,
-                      height: "100%",
-                      background: "var(--accent)",
-                      transition: "width 0.3s",
-                    }}
-                  />
-                </div>
-              </div>
-
-              {/* Batch Progress */}
-              <div>
-                <div
-                  style={{
-                    display: "flex",
-                    justifyContent: "space-between",
-                    fontSize: 12,
-                    marginBottom: 4,
-                  }}
-                >
-                  <span>Batch</span>
-                  <span>
-                    {progress?.batch ?? 0} / {progress?.total_batches ?? 0}
-                  </span>
-                </div>
-                <div
-                  style={{
-                    height: 4,
-                    background: "var(--border)",
-                    borderRadius: 2,
-                    overflow: "hidden",
-                  }}
-                >
-                  <div
-                    style={{
-                      width: `${batchProgress}%`,
-                      height: "100%",
-                      background: "rgba(56, 139, 253, 0.5)",
-                      transition: "width 0.1s",
-                    }}
-                  />
-                </div>
-              </div>
-
-              {/* Stats Grid */}
-              <div
-                style={{
-                  display: "grid",
-                  gridTemplateColumns: "repeat(2, 1fr)",
-                  gap: "var(--space-3)",
-                }}
-              >
-                <div className="card" style={{ padding: "var(--space-3)" }}>
-                  <div style={{ fontSize: 10, color: "var(--text-muted)", marginBottom: 4 }}>
-                    Train Loss
-                  </div>
-                  <div style={{ fontFamily: "var(--font-mono)", fontSize: 20, fontWeight: 600 }}>
-                    {progress?.train_loss.toFixed(4) ?? "—"}
-                  </div>
-                </div>
-                <div className="card" style={{ padding: "var(--space-3)" }}>
-                  <div style={{ fontSize: 10, color: "var(--text-muted)", marginBottom: 4 }}>
-                    Val Loss
-                  </div>
-                  <div
-                    style={{
-                      fontFamily: "var(--font-mono)",
-                      fontSize: 20,
-                      fontWeight: 600,
-                      color: "var(--status-online)",
-                    }}
-                  >
-                    {progress?.val_loss?.toFixed(4) ?? "—"}
-                  </div>
-                </div>
-                <div className="card" style={{ padding: "var(--space-3)" }}>
-                  <div style={{ fontSize: 10, color: "var(--text-muted)", marginBottom: 4 }}>
-                    Learning Rate
-                  </div>
-                  <div style={{ fontFamily: "var(--font-mono)", fontSize: 14, fontWeight: 600 }}>
-                    {progress?.learning_rate.toExponential(2) ?? "—"}
-                  </div>
-                </div>
-                <div className="card" style={{ padding: "var(--space-3)" }}>
-                  <div style={{ fontSize: 10, color: "var(--text-muted)", marginBottom: 4 }}>
-                    GPU Memory
-                  </div>
-                  <div style={{ fontFamily: "var(--font-mono)", fontSize: 14, fontWeight: 600 }}>
-                    {progress?.gpu_memory_mb ? `${progress.gpu_memory_mb} MB` : "N/A"}
-                  </div>
-                </div>
-              </div>
-
-              {/* Mini Loss Chart */}
-              {lossHistory.length > 0 && (
-                <div>
-                  <div style={{ fontSize: 12, fontWeight: 600, marginBottom: "var(--space-2)" }}>
-                    Loss History
-                  </div>
-                  <div
-                    style={{
-                      height: 80,
-                      display: "flex",
-                      alignItems: "flex-end",
-                      gap: 2,
-                      padding: "var(--space-2)",
-                      background: "var(--bg-secondary)",
-                      borderRadius: 4,
-                    }}
-                  >
-                    {lossHistory.slice(-20).map((h, i) => (
-                      <div
-                        key={i}
-                        style={{
-                          flex: 1,
-                          height: `${Math.max(5, Math.min(100, (1 - h.train) * 100))}%`,
-                          background: "var(--accent)",
-                          borderRadius: 2,
-                          opacity: 0.6 + (i / 20) * 0.4,
-                        }}
-                        title={`Epoch ${h.epoch}: Train=${h.train.toFixed(4)}, Val=${h.val.toFixed(4)}`}
-                      />
-                    ))}
-                  </div>
-                </div>
-              )}
-            </div>
-          )}
-        </div>
-      </div>
-
-      <style>{`
-        @keyframes pulse {
-          0%, 100% { opacity: 1; }
-          50% { opacity: 0.5; }
-        }
-      `}</style>
-    </div>
-  );
-};
-
-const labelStyle: React.CSSProperties = {
-  display: "block",
-  fontSize: 11,
-  fontWeight: 600,
-  color: "var(--text-muted)",
-  marginBottom: 4,
-  textTransform: "uppercase",
-  letterSpacing: "0.04em",
-};
-
-const inputStyle: React.CSSProperties = {
-  width: "100%",
-  padding: "8px 12px",
-  background: "var(--bg-secondary)",
-  border: "1px solid var(--border)",
-  borderRadius: 6,
-  color: "var(--text-primary)",
-  fontSize: 13,
-};
-
-export default TrainingTab;
@@ -1,165 +0,0 @@
-import React, { useState, useEffect } from "react";
-import { invoke } from "@tauri-apps/api/core";
-import DatasetsTab from "./DatasetsTab";
-import ModelsTab from "./ModelsTab";
-import TrainingTab from "./TrainingTab";
-import RuVectorTab from "./RuVectorTab";
-import MetricsTab from "./MetricsTab";
-
-type TrainingTabType = "datasets" | "models" | "training" | "ruvector" | "metrics";
-
-interface GpuInfo {
-  available: boolean;
-  name: string | null;
-  memory_mb: number | null;
-  cuda_version: string | null;
-  metal_supported: boolean;
-}
-
-const Training: React.FC = () => {
-  const [activeTab, setActiveTab] = useState<TrainingTabType>("datasets");
-  const [gpuInfo, setGpuInfo] = useState<GpuInfo | null>(null);
-  const [loading, setLoading] = useState(true);
-
-  useEffect(() => {
-    detectGpu();
-  }, []);
-
-  const detectGpu = async () => {
-    try {
-      const info = await invoke<GpuInfo>("detect_gpu");
-      setGpuInfo(info);
-    } catch (err) {
-      console.error("GPU detection failed:", err);
-      setGpuInfo({
-        available: false,
-        name: null,
-        memory_mb: null,
-        cuda_version: null,
-        metal_supported: false,
-      });
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  const tabs: { id: TrainingTabType; label: string; icon: string }[] = [
-    { id: "datasets", label: "Datasets", icon: "📊" },
-    { id: "models", label: "Models", icon: "🧠" },
-    { id: "training", label: "Training", icon: "⚡" },
-    { id: "ruvector", label: "RuVector", icon: "📡" },
-    { id: "metrics", label: "Metrics", icon: "📈" },
-  ];
-
-  return (
-    <div style={{ padding: "var(--space-5)", maxWidth: 1400 }}>
-      {/* Header */}
-      <div
-        style={{
-          display: "flex",
-          justifyContent: "space-between",
-          alignItems: "center",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        <div>
-          <h1 className="heading-lg" style={{ margin: 0 }}>
-            Training & Models
-          </h1>
-          <p
-            style={{
-              fontSize: 13,
-              color: "var(--text-secondary)",
-              marginTop: 4,
-            }}
-          >
-            Train pose estimation models and configure RuVector signal processing
-          </p>
-        </div>
-
-        {/* GPU Status */}
-        <div
-          style={{
-            display: "flex",
-            alignItems: "center",
-            gap: "var(--space-3)",
-            padding: "var(--space-3) var(--space-4)",
-            background: gpuInfo?.available
-              ? "rgba(63, 185, 80, 0.1)"
-              : "rgba(139, 148, 158, 0.1)",
-            border: `1px solid ${gpuInfo?.available ? "rgba(63, 185, 80, 0.3)" : "rgba(139, 148, 158, 0.3)"}`,
-            borderRadius: 8,
-          }}
-        >
-          <span style={{ fontSize: 18 }}>{gpuInfo?.available ? "🎮" : "💻"}</span>
-          <div>
-            <div style={{ fontSize: 12, fontWeight: 600, color: "var(--text-primary)" }}>
-              {loading
-                ? "Detecting GPU..."
-                : gpuInfo?.available
-                  ? gpuInfo.name || "GPU Available"
-                  : "CPU Mode"}
-            </div>
-            <div style={{ fontSize: 11, color: "var(--text-muted)" }}>
-              {gpuInfo?.cuda_version
-                ? `CUDA ${gpuInfo.cuda_version}`
-                : gpuInfo?.metal_supported
-                  ? "Metal Supported"
-                  : "No GPU acceleration"}
-              {gpuInfo?.memory_mb && ` • ${Math.round(gpuInfo.memory_mb / 1024)}GB`}
-            </div>
-          </div>
-        </div>
-      </div>
-
-      {/* Tabs */}
-      <div
-        style={{
-          display: "flex",
-          gap: "var(--space-1)",
-          borderBottom: "1px solid var(--border)",
-          marginBottom: "var(--space-5)",
-        }}
-      >
-        {tabs.map((tab) => (
-          <button
-            key={tab.id}
-            onClick={() => setActiveTab(tab.id)}
-            style={{
-              display: "flex",
-              alignItems: "center",
-              gap: 6,
-              padding: "12px 20px",
-              border: "none",
-              background: "transparent",
-              color: activeTab === tab.id ? "var(--accent)" : "var(--text-secondary)",
-              fontSize: 13,
-              fontWeight: 600,
-              cursor: "pointer",
-              borderBottom:
-                activeTab === tab.id
-                  ? "2px solid var(--accent)"
-                  : "2px solid transparent",
-              marginBottom: -1,
-              transition: "color 0.15s, border-color 0.15s",
-            }}
-          >
-            <span>{tab.icon}</span>
-            {tab.label}
-          </button>
-        ))}
-      </div>
-
-      {/* Tab Content */}
-      <div>
-        {activeTab === "datasets" && <DatasetsTab />}
-        {activeTab === "models" && <ModelsTab />}
-        {activeTab === "training" && <TrainingTab gpuAvailable={gpuInfo?.available ?? false} />}
-        {activeTab === "ruvector" && <RuVectorTab />}
-        {activeTab === "metrics" && <MetricsTab />}
-      </div>
-    </div>
-  );
-};
-
-export default Training;
@@ -1,2 +1,2 @@
 // Application version - single source of truth
-export const APP_VERSION = "0.5.0";
+export const APP_VERSION = "0.4.4";
@@ -29,6 +29,7 @@
            <button class="nav-tab" data-tab="applications">Applications</button>
            <button class="nav-tab" data-tab="sensing">Sensing</button>
            <button class="nav-tab" data-tab="training">Training</button>
+            <a href="pose-fusion.html" class="nav-tab" style="text-decoration:none">Pose Fusion</a>
            <a href="observatory.html" class="nav-tab" style="text-decoration:none">Observatory</a>
        </nav>

@@ -0,0 +1,160 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>WiFi-DensePose — Dual-Modal Pose Estimation</title>
+  <link rel="stylesheet" href="pose-fusion/css/style.css">
+</head>
+<body>
+
+  <!-- Header -->
+  <header class="header">
+    <div class="header-left">
+      <div class="logo"><span class="pi">&pi;</span> DensePose</div>
+      <div class="header-title">Dual-Modal Pose Estimation — Live Video + WiFi CSI Fusion</div>
+    </div>
+    <div class="header-right">
+      <select id="mode-select" class="mode-select">
+        <option value="dual">Dual Mode (Video + CSI)</option>
+        <option value="video">Video Only</option>
+        <option value="csi">CSI Only (WiFi)</option>
+      </select>
+      <div class="status-badge">
+        <span id="status-dot" class="status-dot offline"></span>
+        <span id="status-label">READY</span>
+      </div>
+      <span id="fps-display" class="fps-badge">-- FPS</span>
+      <a href="index.html" class="back-link">&larr; Dashboard</a>
+      <a href="observatory.html" class="back-link">Observatory &rarr;</a>
+    </div>
+  </header>
+
+  <!-- Main Grid -->
+  <div class="main-grid">
+
+    <!-- Video + Skeleton Panel -->
+    <div class="video-panel">
+      <video id="webcam" autoplay playsinline muted></video>
+      <canvas id="skeleton-canvas"></canvas>
+      <div class="video-overlay-label" id="mode-label">DUAL FUSION</div>
+
+      <div id="camera-prompt" class="camera-prompt">
+        <p>Enable your webcam for live video pose estimation.<br>
+           Or switch to <strong>CSI Only</strong> mode for WiFi-based sensing.</p>
+        <button id="start-camera-btn">Enable Camera</button>
+      </div>
+    </div>
+
+    <!-- Side Panels -->
+    <div class="side-panels">
+
+      <!-- Fusion Confidence -->
+      <div class="panel">
+        <div class="panel-title">&#9670; Fusion Confidence</div>
+        <div class="fusion-bars">
+          <div class="bar-row">
+            <span class="bar-label">Video</span>
+            <div class="bar-track"><div class="bar-fill video" id="video-bar" style="width:0%"></div></div>
+            <span class="bar-value" id="video-bar-val">0%</span>
+          </div>
+          <div class="bar-row">
+            <span class="bar-label">CSI</span>
+            <div class="bar-track"><div class="bar-fill csi" id="csi-bar" style="width:0%"></div></div>
+            <span class="bar-value" id="csi-bar-val">0%</span>
+          </div>
+          <div class="bar-row">
+            <span class="bar-label">Fused</span>
+            <div class="bar-track"><div class="bar-fill fused" id="fused-bar" style="width:0%"></div></div>
+            <span class="bar-value" id="fused-bar-val">0%</span>
+          </div>
+        </div>
+        <div style="margin-top:8px; font-size:10px; color:var(--text-label)">
+          Cross-modal: <span id="cross-modal-sim" style="color:var(--green-glow)">0.000</span>
+        </div>
+      </div>
+
+      <!-- CSI Heatmap -->
+      <div class="panel">
+        <div class="panel-title">&#9670; CSI Amplitude Heatmap</div>
+        <div class="csi-canvas-wrapper">
+          <canvas id="csi-canvas" width="320" height="120"></canvas>
+        </div>
+      </div>
+
+      <!-- Embedding Space -->
+      <div class="panel">
+        <div class="panel-title">&#9670; Embedding Space (2D Projection)</div>
+        <div class="embedding-canvas-wrapper">
+          <canvas id="embedding-canvas" width="320" height="140"></canvas>
+        </div>
+      </div>
+
+      <!-- Latency -->
+      <div class="panel">
+        <div class="panel-title">&#9670; Pipeline Latency</div>
+        <div class="latency-grid">
+          <div class="latency-item">
+            <div class="latency-value" id="lat-video">--</div>
+            <div class="latency-label">Video CNN</div>
+          </div>
+          <div class="latency-item">
+            <div class="latency-value" id="lat-csi">--</div>
+            <div class="latency-label">CSI CNN</div>
+          </div>
+          <div class="latency-item">
+            <div class="latency-value" id="lat-fusion">--</div>
+            <div class="latency-label">Fusion</div>
+          </div>
+          <div class="latency-item">
+            <div class="latency-value" id="lat-total">--</div>
+            <div class="latency-label">Total</div>
+          </div>
+        </div>
+      </div>
+
+      <!-- Controls -->
+      <div class="panel">
+        <div class="panel-title">&#9670; Controls</div>
+        <div class="controls-row">
+          <button class="btn" id="pause-btn">⏸ Pause</button>
+        </div>
+
+        <div class="slider-row">
+          <label>Confidence</label>
+          <input type="range" id="confidence-slider" min="0" max="1" step="0.05" value="0.3">
+          <span class="slider-val" id="confidence-value">0.30</span>
+        </div>
+
+        <div style="margin-top:10px">
+          <div class="panel-title" style="margin-bottom:6px">&#9670; Live CSI Source</div>
+          <div style="display:flex;gap:6px">
+            <input type="text" id="ws-url" placeholder="ws://localhost:3030/ws/csi"
+              style="flex:1;background:rgba(255,255,255,0.05);border:1px solid var(--bg-panel-border);
+                     color:var(--text-primary);padding:5px 8px;border-radius:4px;font-size:11px;
+                     font-family:'JetBrains Mono',monospace">
+            <button class="btn" id="connect-ws-btn">Connect</button>
+          </div>
+        </div>
+      </div>
+
+    </div><!-- /side-panels -->
+
+    <!-- Bottom Bar -->
+    <div class="bottom-bar">
+      <div>
+        WiFi-DensePose &middot; Dual-Modal Pose Estimation &middot;
+        Architecture: MobileNet-V3 &times; 2 &rarr; Attention Fusion &rarr; 17-Keypoint COCO
+      </div>
+      <div>
+        <a href="https://github.com/ruvnet/wifi-densepose">GitHub</a> &middot;
+        CNN: ruvector-cnn (JS fallback) &middot;
+        <a href="observatory.html">Observatory</a>
+      </div>
+    </div>
+
+  </div><!-- /main-grid -->
+
+  <script type="module" src="pose-fusion/js/main.js"></script>
+</body>
+</html>
@@ -0,0 +1,30 @@
+#!/bin/bash
+# Build WASM packages for the dual-modal pose estimation demo.
+# Requires: wasm-pack (cargo install wasm-pack)
+#
+# Usage: ./build.sh
+#
+# Output: pkg/ruvector_cnn_wasm/ — WASM CNN embedder for browser
+
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+VENDOR_DIR="$SCRIPT_DIR/../../vendor/ruvector"
+OUT_DIR="$SCRIPT_DIR/pkg/ruvector_cnn_wasm"
+
+echo "Building ruvector-cnn-wasm..."
+wasm-pack build "$VENDOR_DIR/crates/ruvector-cnn-wasm" \
+  --target web \
+  --out-dir "$OUT_DIR" \
+  --no-typescript
+
+# Remove .gitignore so we can commit the build output for GitHub Pages
+rm -f "$OUT_DIR/.gitignore"
+
+echo ""
+echo "Build complete!"
+echo "  WASM: $(du -sh "$OUT_DIR/ruvector_cnn_wasm_bg.wasm" | cut -f1)"
+echo "  JS:   $(du -sh "$OUT_DIR/ruvector_cnn_wasm.js" | cut -f1)"
+echo ""
+echo "Serve the demo: cd $SCRIPT_DIR/.. && python3 -m http.server 8080"
+echo "Open: http://localhost:8080/pose-fusion.html"
@@ -0,0 +1,405 @@
+/* WiFi-DensePose — Dual-Modal Pose Fusion Demo
+   Dark theme matching Observatory */
+
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=JetBrains+Mono:wght@400;600&display=swap');
+
+:root {
+  --bg-deep:     #080c14;
+  --bg-panel:    rgba(8, 16, 28, 0.92);
+  --bg-panel-border: rgba(0, 210, 120, 0.25);
+  --green-glow:  #00d878;
+  --green-bright:#3eff8a;
+  --green-dim:   #0a6b3a;
+  --amber:       #ffb020;
+  --amber-dim:   #a06800;
+  --blue-signal: #2090ff;
+  --blue-dim:    #0a3060;
+  --red-alert:   #ff3040;
+  --cyan:        #00e5ff;
+  --text-primary:   #e8ece0;
+  --text-secondary: rgba(232,236,224, 0.55);
+  --text-label:     rgba(232,236,224, 0.35);
+  --radius: 8px;
+}
+
+* { margin: 0; padding: 0; box-sizing: border-box; }
+
+body {
+  background: var(--bg-deep);
+  font-family: 'Inter', -apple-system, sans-serif;
+  color: var(--text-primary);
+  -webkit-font-smoothing: antialiased;
+  overflow-x: hidden;
+  min-height: 100vh;
+}
+
+/* === Header === */
+.header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 16px 24px;
+  border-bottom: 1px solid var(--bg-panel-border);
+  background: var(--bg-panel);
+  backdrop-filter: blur(12px);
+}
+
+.header-left {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+}
+
+.logo {
+  font-weight: 700;
+  font-size: 24px;
+  color: var(--green-glow);
+}
+
+.logo .pi { font-style: normal; }
+
+.header-title {
+  font-size: 14px;
+  color: var(--text-secondary);
+  font-weight: 300;
+}
+
+.header-right {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+}
+
+.mode-select {
+  background: rgba(0,210,120,0.1);
+  border: 1px solid var(--bg-panel-border);
+  color: var(--text-primary);
+  padding: 6px 12px;
+  border-radius: var(--radius);
+  font-family: inherit;
+  font-size: 13px;
+  cursor: pointer;
+}
+
+.mode-select option { background: #0c1420; }
+
+.status-badge {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 12px;
+  padding: 4px 10px;
+  border-radius: 12px;
+  background: rgba(0,210,120,0.1);
+  border: 1px solid var(--bg-panel-border);
+}
+
+.status-dot {
+  width: 8px; height: 8px;
+  border-radius: 50%;
+  background: var(--green-glow);
+  box-shadow: 0 0 8px var(--green-glow);
+  animation: pulse-dot 2s ease infinite;
+}
+
+.status-dot.offline { background: #555; box-shadow: none; animation: none; }
+.status-dot.warning { background: var(--amber); box-shadow: 0 0 8px var(--amber); }
+
+@keyframes pulse-dot {
+  0%, 100% { opacity: 1; }
+  50% { opacity: 0.5; }
+}
+
+.fps-badge {
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 12px;
+  color: var(--green-glow);
+}
+
+.back-link {
+  color: var(--text-secondary);
+  text-decoration: none;
+  font-size: 13px;
+  transition: color 0.2s;
+}
+.back-link:hover { color: var(--green-glow); }
+
+/* === Main Layout === */
+.main-grid {
+  display: grid;
+  grid-template-columns: 1fr 360px;
+  grid-template-rows: 1fr auto;
+  gap: 16px;
+  padding: 16px 24px;
+  height: calc(100vh - 72px);
+  overflow: hidden;
+}
+
+/* === Video Panel === */
+.video-panel {
+  position: relative;
+  background: #000;
+  border-radius: var(--radius);
+  border: 1px solid var(--bg-panel-border);
+  overflow: hidden;
+  min-height: 0;
+}
+
+.video-panel video {
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+  transform: scaleX(-1);
+}
+
+.video-panel canvas {
+  position: absolute;
+  top: 0; left: 0;
+  width: 100%;
+  height: 100%;
+  transform: scaleX(-1);
+}
+
+.video-overlay-label {
+  position: absolute;
+  top: 12px; left: 12px;
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 11px;
+  padding: 4px 8px;
+  background: rgba(0,0,0,0.7);
+  border-radius: 4px;
+  color: var(--green-glow);
+  z-index: 5;
+  transform: scaleX(-1);
+}
+
+.camera-prompt {
+  position: absolute;
+  top: 50%; left: 50%;
+  transform: translate(-50%, -50%);
+  text-align: center;
+  color: var(--text-secondary);
+}
+
+.camera-prompt button {
+  margin-top: 12px;
+  padding: 10px 24px;
+  background: var(--green-glow);
+  color: #000;
+  border: none;
+  border-radius: var(--radius);
+  font-family: inherit;
+  font-weight: 600;
+  font-size: 14px;
+  cursor: pointer;
+  transition: background 0.2s;
+}
+
+.camera-prompt button:hover { background: var(--green-bright); }
+
+/* === Side Panels === */
+.side-panels {
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+  overflow-y: auto;
+  min-height: 0;
+}
+
+.panel {
+  background: var(--bg-panel);
+  border: 1px solid var(--bg-panel-border);
+  border-radius: var(--radius);
+  padding: 14px;
+}
+
+.panel-title {
+  font-size: 11px;
+  text-transform: uppercase;
+  letter-spacing: 1.2px;
+  color: var(--text-label);
+  margin-bottom: 10px;
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+
+/* === CSI Heatmap === */
+.csi-canvas-wrapper {
+  position: relative;
+  border-radius: 4px;
+  overflow: hidden;
+  background: #000;
+}
+
+.csi-canvas-wrapper canvas {
+  width: 100%;
+  display: block;
+}
+
+/* === Fusion Bars === */
+.fusion-bars {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.bar-row {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.bar-label {
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 11px;
+  color: var(--text-secondary);
+  width: 55px;
+  text-align: right;
+}
+
+.bar-track {
+  flex: 1;
+  height: 6px;
+  background: rgba(255,255,255,0.06);
+  border-radius: 3px;
+  overflow: hidden;
+}
+
+.bar-fill {
+  height: 100%;
+  border-radius: 3px;
+  transition: width 0.3s ease;
+}
+
+.bar-fill.video { background: var(--cyan); }
+.bar-fill.csi { background: var(--amber); }
+.bar-fill.fused { background: var(--green-glow); box-shadow: 0 0 8px var(--green-glow); }
+
+.bar-value {
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 11px;
+  color: var(--text-primary);
+  width: 36px;
+}
+
+/* === Embedding Space === */
+.embedding-canvas-wrapper {
+  position: relative;
+  background: #000;
+  border-radius: 4px;
+  overflow: hidden;
+}
+.embedding-canvas-wrapper canvas {
+  width: 100%;
+  display: block;
+}
+
+/* === Latency Panel === */
+.latency-grid {
+  display: grid;
+  grid-template-columns: repeat(4, 1fr);
+  gap: 6px;
+}
+
+.latency-item {
+  text-align: center;
+  padding: 6px 0;
+}
+
+.latency-value {
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 16px;
+  font-weight: 600;
+  color: var(--green-glow);
+}
+
+.latency-label {
+  font-size: 10px;
+  color: var(--text-label);
+  margin-top: 2px;
+}
+
+/* === Controls === */
+.controls-row {
+  display: flex;
+  gap: 8px;
+  flex-wrap: wrap;
+}
+
+.btn {
+  padding: 6px 14px;
+  border: 1px solid var(--bg-panel-border);
+  background: rgba(0,210,120,0.08);
+  color: var(--text-primary);
+  border-radius: var(--radius);
+  font-family: inherit;
+  font-size: 12px;
+  cursor: pointer;
+  transition: all 0.2s;
+}
+.btn:hover { background: rgba(0,210,120,0.2); }
+.btn.active { background: var(--green-glow); color: #000; font-weight: 600; }
+
+.slider-row {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  margin-top: 8px;
+}
+
+.slider-row label {
+  font-size: 11px;
+  color: var(--text-secondary);
+  white-space: nowrap;
+}
+
+.slider-row input[type=range] {
+  flex: 1;
+  accent-color: var(--green-glow);
+}
+
+.slider-row .slider-val {
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 11px;
+  width: 32px;
+  color: var(--green-glow);
+}
+
+/* === Bottom Bar === */
+.bottom-bar {
+  grid-column: 1 / -1;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding: 10px 16px;
+  background: var(--bg-panel);
+  border: 1px solid var(--bg-panel-border);
+  border-radius: var(--radius);
+  font-family: 'JetBrains Mono', monospace;
+  font-size: 11px;
+  color: var(--text-secondary);
+}
+
+.bottom-bar a {
+  color: var(--green-glow);
+  text-decoration: none;
+}
+
+/* === Skeleton colors === */
+.skeleton-joint { fill: var(--green-glow); }
+.skeleton-limb { stroke: var(--green-bright); }
+.skeleton-joint-csi { fill: var(--amber); }
+.skeleton-limb-csi { stroke: var(--amber); }
+
+/* === Responsive === */
+@media (max-width: 900px) {
+  .main-grid {
+    grid-template-columns: 1fr;
+    height: auto;
+    overflow: auto;
+  }
+  .video-panel { aspect-ratio: 16/9; max-height: 50vh; }
+  .side-panels { max-height: none; overflow: visible; }
+}
@@ -0,0 +1,247 @@
+/**
+ * CanvasRenderer — Renders skeleton overlay on video, CSI heatmap,
+ * embedding space visualization, and fusion confidence bars.
+ */
+
+import { SKELETON_CONNECTIONS } from './pose-decoder.js';
+
+export class CanvasRenderer {
+  constructor() {
+    this.colors = {
+      joint:      '#00d878',
+      jointGlow:  'rgba(0, 216, 120, 0.4)',
+      limb:       '#3eff8a',
+      limbGlow:   'rgba(62, 255, 138, 0.15)',
+      csiJoint:   '#ffb020',
+      csiLimb:    '#ffc850',
+      fused:      '#00e5ff',
+      confidence: 'rgba(255,255,255,0.3)',
+      videoEmb:   '#00e5ff',
+      csiEmb:     '#ffb020',
+      fusedEmb:   '#00d878',
+    };
+  }
+
+  /**
+   * Draw skeleton overlay on the video canvas
+   * @param {CanvasRenderingContext2D} ctx
+   * @param {Array<{x,y,confidence}>} keypoints - Normalized [0,1] coordinates
+   * @param {number} width - Canvas width
+   * @param {number} height - Canvas height
+   * @param {object} opts
+   */
+  drawSkeleton(ctx, keypoints, width, height, opts = {}) {
+    const minConf = opts.minConfidence || 0.3;
+    const color = opts.color || 'green';
+    const jointColor = color === 'amber' ? this.colors.csiJoint : this.colors.joint;
+    const limbColor = color === 'amber' ? this.colors.csiLimb : this.colors.limb;
+    const glowColor = color === 'amber' ? 'rgba(255,176,32,0.4)' : this.colors.jointGlow;
+
+    ctx.clearRect(0, 0, width, height);
+
+    if (!keypoints || keypoints.length === 0) return;
+
+    // Draw limbs first (behind joints)
+    ctx.lineWidth = 3;
+    ctx.lineCap = 'round';
+
+    for (const [i, j] of SKELETON_CONNECTIONS) {
+      const kpA = keypoints[i];
+      const kpB = keypoints[j];
+      if (!kpA || !kpB || kpA.confidence < minConf || kpB.confidence < minConf) continue;
+
+      const ax = kpA.x * width, ay = kpA.y * height;
+      const bx = kpB.x * width, by = kpB.y * height;
+      const avgConf = (kpA.confidence + kpB.confidence) / 2;
+
+      // Glow
+      ctx.strokeStyle = this.colors.limbGlow;
+      ctx.lineWidth = 8;
+      ctx.globalAlpha = avgConf * 0.4;
+      ctx.beginPath();
+      ctx.moveTo(ax, ay);
+      ctx.lineTo(bx, by);
+      ctx.stroke();
+
+      // Main line
+      ctx.strokeStyle = limbColor;
+      ctx.lineWidth = 2.5;
+      ctx.globalAlpha = avgConf;
+      ctx.beginPath();
+      ctx.moveTo(ax, ay);
+      ctx.lineTo(bx, by);
+      ctx.stroke();
+    }
+
+    // Draw joints
+    ctx.globalAlpha = 1;
+    for (const kp of keypoints) {
+      if (!kp || kp.confidence < minConf) continue;
+
+      const x = kp.x * width;
+      const y = kp.y * height;
+      const r = 3 + kp.confidence * 3;
+
+      // Glow
+      ctx.beginPath();
+      ctx.arc(x, y, r + 4, 0, Math.PI * 2);
+      ctx.fillStyle = glowColor;
+      ctx.globalAlpha = kp.confidence * 0.6;
+      ctx.fill();
+
+      // Joint dot
+      ctx.beginPath();
+      ctx.arc(x, y, r, 0, Math.PI * 2);
+      ctx.fillStyle = jointColor;
+      ctx.globalAlpha = kp.confidence;
+      ctx.fill();
+
+      // White center
+      ctx.beginPath();
+      ctx.arc(x, y, r * 0.4, 0, Math.PI * 2);
+      ctx.fillStyle = '#fff';
+      ctx.globalAlpha = kp.confidence * 0.8;
+      ctx.fill();
+    }
+
+    ctx.globalAlpha = 1;
+
+    // Confidence label
+    if (opts.label) {
+      ctx.font = '11px "JetBrains Mono", monospace';
+      ctx.fillStyle = jointColor;
+      ctx.globalAlpha = 0.8;
+      ctx.fillText(opts.label, 8, height - 8);
+      ctx.globalAlpha = 1;
+    }
+  }
+
+  /**
+   * Draw CSI amplitude heatmap
+   * @param {CanvasRenderingContext2D} ctx
+   * @param {{ data: Float32Array, width: number, height: number }} heatmap
+   * @param {number} canvasW
+   * @param {number} canvasH
+   */
+  drawCsiHeatmap(ctx, heatmap, canvasW, canvasH) {
+    ctx.clearRect(0, 0, canvasW, canvasH);
+
+    if (!heatmap || !heatmap.data || heatmap.height < 2) {
+      ctx.fillStyle = '#0a0e18';
+      ctx.fillRect(0, 0, canvasW, canvasH);
+      ctx.font = '11px "JetBrains Mono", monospace';
+      ctx.fillStyle = 'rgba(255,255,255,0.3)';
+      ctx.fillText('Waiting for CSI data...', 8, canvasH / 2);
+      return;
+    }
+
+    const { data, width: dw, height: dh } = heatmap;
+    const cellW = canvasW / dw;
+    const cellH = canvasH / dh;
+
+    for (let y = 0; y < dh; y++) {
+      for (let x = 0; x < dw; x++) {
+        const val = Math.min(1, Math.max(0, data[y * dw + x]));
+        ctx.fillStyle = this._heatmapColor(val);
+        ctx.fillRect(x * cellW, y * cellH, cellW + 0.5, cellH + 0.5);
+      }
+    }
+
+    // Axis labels
+    ctx.font = '9px "JetBrains Mono", monospace';
+    ctx.fillStyle = 'rgba(255,255,255,0.4)';
+    ctx.fillText('Subcarrier →', 4, canvasH - 4);
+    ctx.save();
+    ctx.translate(canvasW - 4, canvasH - 4);
+    ctx.rotate(-Math.PI / 2);
+    ctx.fillText('Time ↑', 0, 0);
+    ctx.restore();
+  }
+
+  /**
+   * Draw embedding space 2D projection
+   * @param {CanvasRenderingContext2D} ctx
+   * @param {{ video: Array, csi: Array, fused: Array }} points
+   * @param {number} w
+   * @param {number} h
+   */
+  drawEmbeddingSpace(ctx, points, w, h) {
+    ctx.fillStyle = '#050810';
+    ctx.fillRect(0, 0, w, h);
+
+    // Grid
+    ctx.strokeStyle = 'rgba(255,255,255,0.05)';
+    ctx.lineWidth = 0.5;
+    for (let i = 0; i <= 4; i++) {
+      const x = (i / 4) * w;
+      ctx.beginPath(); ctx.moveTo(x, 0); ctx.lineTo(x, h); ctx.stroke();
+      const y = (i / 4) * h;
+      ctx.beginPath(); ctx.moveTo(0, y); ctx.lineTo(w, y); ctx.stroke();
+    }
+
+    // Axes
+    ctx.strokeStyle = 'rgba(255,255,255,0.1)';
+    ctx.lineWidth = 1;
+    ctx.beginPath(); ctx.moveTo(w / 2, 0); ctx.lineTo(w / 2, h); ctx.stroke();
+    ctx.beginPath(); ctx.moveTo(0, h / 2); ctx.lineTo(w, h / 2); ctx.stroke();
+
+    const drawPoints = (pts, color, size) => {
+      if (!pts || pts.length === 0) return;
+      const len = pts.length;
+      for (let i = 0; i < len; i++) {
+        const p = pts[i];
+        if (!p) continue;
+        const age = 1 - (i / len) * 0.7; // Fade older points
+        const px = w / 2 + p[0] * w * 0.35;
+        const py = h / 2 + p[1] * h * 0.35;
+
+        if (px < 0 || px > w || py < 0 || py > h) continue;
+
+        ctx.beginPath();
+        ctx.arc(px, py, size, 0, Math.PI * 2);
+        ctx.fillStyle = color;
+        ctx.globalAlpha = age * 0.7;
+        ctx.fill();
+      }
+    };
+
+    drawPoints(points.video, this.colors.videoEmb, 3);
+    drawPoints(points.csi, this.colors.csiEmb, 3);
+    drawPoints(points.fused, this.colors.fusedEmb, 4);
+    ctx.globalAlpha = 1;
+
+    // Legend
+    ctx.font = '9px "JetBrains Mono", monospace';
+    const legends = [
+      { color: this.colors.videoEmb, label: 'Video' },
+      { color: this.colors.csiEmb, label: 'CSI' },
+      { color: this.colors.fusedEmb, label: 'Fused' },
+    ];
+    legends.forEach((l, i) => {
+      const ly = 12 + i * 14;
+      ctx.fillStyle = l.color;
+      ctx.beginPath();
+      ctx.arc(10, ly - 3, 3, 0, Math.PI * 2);
+      ctx.fill();
+      ctx.fillStyle = 'rgba(255,255,255,0.5)';
+      ctx.fillText(l.label, 18, ly);
+    });
+  }
+
+  _heatmapColor(val) {
+    // Dark blue → cyan → green → yellow → red
+    if (val < 0.25) {
+      const t = val / 0.25;
+      return `rgb(${Math.floor(t * 20)}, ${Math.floor(20 + t * 60)}, ${Math.floor(60 + t * 100)})`;
+    } else if (val < 0.5) {
+      const t = (val - 0.25) / 0.25;
+      return `rgb(${Math.floor(20 + t * 20)}, ${Math.floor(80 + t * 100)}, ${Math.floor(160 - t * 60)})`;
+    } else if (val < 0.75) {
+      const t = (val - 0.5) / 0.25;
+      return `rgb(${Math.floor(40 + t * 180)}, ${Math.floor(180 + t * 75)}, ${Math.floor(100 - t * 80)})`;
+    } else {
+      const t = (val - 0.75) / 0.25;
+      return `rgb(${Math.floor(220 + t * 35)}, ${Math.floor(255 - t * 120)}, ${Math.floor(20 - t * 20)})`;
+    }
+  }
+}
@@ -0,0 +1,226 @@
+/**
+ * CNN Embedder — Lightweight MobileNet-V3-style feature extractor.
+ *
+ * Architecture mirrors ruvector-cnn: Conv2D → BatchNorm → ReLU → Pool → Project → L2 Normalize
+ * Uses pre-seeded random weights (deterministic). When ruvector-cnn-wasm is available,
+ * transparently delegates to the WASM implementation.
+ *
+ * Two instances are created: one for video frames, one for CSI pseudo-images.
+ */
+
+// Seeded PRNG for deterministic weight initialization
+function mulberry32(seed) {
+  return function() {
+    let t = (seed += 0x6D2B79F5);
+    t = Math.imul(t ^ (t >>> 15), t | 1);
+    t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
+    return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+  };
+}
+
+export class CnnEmbedder {
+  /**
+   * @param {object} opts
+   * @param {number} opts.inputSize   - Square input dimension (default 56 for speed)
+   * @param {number} opts.embeddingDim - Output embedding dimension (default 128)
+   * @param {boolean} opts.normalize  - L2 normalize output
+   * @param {number} opts.seed        - PRNG seed for weight init
+   */
+  constructor(opts = {}) {
+    this.inputSize = opts.inputSize || 56;
+    this.embeddingDim = opts.embeddingDim || 128;
+    this.normalize = opts.normalize !== false;
+    this.wasmEmbedder = null;
+
+    // Initialize weights with deterministic PRNG
+    const rng = mulberry32(opts.seed || 42);
+    const randRange = (lo, hi) => lo + rng() * (hi - lo);
+
+    // Conv 3x3: 3 input channels → 16 output channels
+    this.convWeights = new Float32Array(3 * 3 * 3 * 16);
+    for (let i = 0; i < this.convWeights.length; i++) {
+      this.convWeights[i] = randRange(-0.15, 0.15);
+    }
+
+    // BatchNorm params (16 channels)
+    this.bnGamma = new Float32Array(16).fill(1.0);
+    this.bnBeta = new Float32Array(16).fill(0.0);
+    this.bnMean = new Float32Array(16).fill(0.0);
+    this.bnVar = new Float32Array(16).fill(1.0);
+
+    // Projection: 16 → embeddingDim
+    this.projWeights = new Float32Array(16 * this.embeddingDim);
+    for (let i = 0; i < this.projWeights.length; i++) {
+      this.projWeights[i] = randRange(-0.1, 0.1);
+    }
+  }
+
+  /**
+   * Try to load WASM embedder from ruvector-cnn-wasm package
+   * @param {string} wasmPath - Path to the WASM package directory
+   */
+  async tryLoadWasm(wasmPath) {
+    try {
+      const mod = await import(`${wasmPath}/ruvector_cnn_wasm.js`);
+      await mod.default();
+      const config = new mod.EmbedderConfig();
+      config.input_size = this.inputSize;
+      config.embedding_dim = this.embeddingDim;
+      config.normalize = this.normalize;
+      this.wasmEmbedder = new mod.WasmCnnEmbedder(config);
+      console.log('[CNN] WASM embedder loaded successfully');
+      return true;
+    } catch (e) {
+      console.log('[CNN] WASM not available, using JS fallback:', e.message);
+      return false;
+    }
+  }
+
+  /**
+   * Extract embedding from RGB image data
+   * @param {Uint8Array} rgbData - RGB pixel data (H*W*3)
+   * @param {number} width
+   * @param {number} height
+   * @returns {Float32Array} embedding vector
+   */
+  extract(rgbData, width, height) {
+    if (this.wasmEmbedder) {
+      try {
+        const result = this.wasmEmbedder.extract(rgbData, width, height);
+        return new Float32Array(result);
+      } catch (_) { /* fallback to JS */ }
+    }
+    return this._extractJS(rgbData, width, height);
+  }
+
+  _extractJS(rgbData, width, height) {
+    // 1. Resize to inputSize × inputSize if needed
+    const sz = this.inputSize;
+    let input;
+    if (width === sz && height === sz) {
+      input = new Float32Array(rgbData.length);
+      for (let i = 0; i < rgbData.length; i++) input[i] = rgbData[i] / 255.0;
+    } else {
+      input = this._resize(rgbData, width, height, sz, sz);
+    }
+
+    // 2. ImageNet normalization
+    const mean = [0.485, 0.456, 0.406];
+    const std = [0.229, 0.224, 0.225];
+    const pixels = sz * sz;
+    for (let i = 0; i < pixels; i++) {
+      input[i * 3]     = (input[i * 3]     - mean[0]) / std[0];
+      input[i * 3 + 1] = (input[i * 3 + 1] - mean[1]) / std[1];
+      input[i * 3 + 2] = (input[i * 3 + 2] - mean[2]) / std[2];
+    }
+
+    // 3. Conv2D 3x3 (3 → 16 channels)
+    const convOut = this._conv2d3x3(input, sz, sz, 3, 16);
+
+    // 4. BatchNorm
+    this._batchNorm(convOut, 16);
+
+    // 5. ReLU
+    for (let i = 0; i < convOut.length; i++) {
+      if (convOut[i] < 0) convOut[i] = 0;
+    }
+
+    // 6. Global average pooling → 16-dim
+    const outH = sz - 2, outW = sz - 2;
+    const pooled = new Float32Array(16);
+    const spatial = outH * outW;
+    for (let i = 0; i < spatial; i++) {
+      for (let c = 0; c < 16; c++) {
+        pooled[c] += convOut[i * 16 + c];
+      }
+    }
+    for (let c = 0; c < 16; c++) pooled[c] /= spatial;
+
+    // 7. Linear projection → embeddingDim
+    const emb = new Float32Array(this.embeddingDim);
+    for (let o = 0; o < this.embeddingDim; o++) {
+      let sum = 0;
+      for (let i = 0; i < 16; i++) {
+        sum += pooled[i] * this.projWeights[i * this.embeddingDim + o];
+      }
+      emb[o] = sum;
+    }
+
+    // 8. L2 normalize
+    if (this.normalize) {
+      let norm = 0;
+      for (let i = 0; i < emb.length; i++) norm += emb[i] * emb[i];
+      norm = Math.sqrt(norm);
+      if (norm > 1e-8) {
+        for (let i = 0; i < emb.length; i++) emb[i] /= norm;
+      }
+    }
+
+    return emb;
+  }
+
+  _conv2d3x3(input, H, W, Cin, Cout) {
+    const outH = H - 2, outW = W - 2;
+    const output = new Float32Array(outH * outW * Cout);
+    for (let y = 0; y < outH; y++) {
+      for (let x = 0; x < outW; x++) {
+        for (let co = 0; co < Cout; co++) {
+          let sum = 0;
+          for (let ky = 0; ky < 3; ky++) {
+            for (let kx = 0; kx < 3; kx++) {
+              for (let ci = 0; ci < Cin; ci++) {
+                const px = ((y + ky) * W + (x + kx)) * Cin + ci;
+                const wt = (((ky * 3 + kx) * Cin) + ci) * Cout + co;
+                sum += input[px] * this.convWeights[wt];
+              }
+            }
+          }
+          output[(y * outW + x) * Cout + co] = sum;
+        }
+      }
+    }
+    return output;
+  }
+
+  _batchNorm(data, channels) {
+    const spatial = data.length / channels;
+    for (let i = 0; i < spatial; i++) {
+      for (let c = 0; c < channels; c++) {
+        const idx = i * channels + c;
+        data[idx] = this.bnGamma[c] * (data[idx] - this.bnMean[c]) / Math.sqrt(this.bnVar[c] + 1e-5) + this.bnBeta[c];
+      }
+    }
+  }
+
+  _resize(rgbData, srcW, srcH, dstW, dstH) {
+    const output = new Float32Array(dstW * dstH * 3);
+    const xRatio = srcW / dstW;
+    const yRatio = srcH / dstH;
+    for (let y = 0; y < dstH; y++) {
+      for (let x = 0; x < dstW; x++) {
+        const sx = Math.min(Math.floor(x * xRatio), srcW - 1);
+        const sy = Math.min(Math.floor(y * yRatio), srcH - 1);
+        const srcIdx = (sy * srcW + sx) * 3;
+        const dstIdx = (y * dstW + x) * 3;
+        output[dstIdx]     = rgbData[srcIdx]     / 255.0;
+        output[dstIdx + 1] = rgbData[srcIdx + 1] / 255.0;
+        output[dstIdx + 2] = rgbData[srcIdx + 2] / 255.0;
+      }
+    }
+    return output;
+  }
+
+  /** Cosine similarity between two embeddings */
+  static cosineSimilarity(a, b) {
+    let dot = 0, normA = 0, normB = 0;
+    for (let i = 0; i < a.length; i++) {
+      dot += a[i] * b[i];
+      normA += a[i] * a[i];
+      normB += b[i] * b[i];
+    }
+    normA = Math.sqrt(normA);
+    normB = Math.sqrt(normB);
+    if (normA < 1e-8 || normB < 1e-8) return 0;
+    return dot / (normA * normB);
+  }
+}
@@ -0,0 +1,267 @@
+/**
+ * CSI Simulator — Generates realistic WiFi Channel State Information data.
+ *
+ * In live mode, connects to the sensing server via WebSocket.
+ * In demo mode, generates synthetic CSI that correlates with detected motion.
+ *
+ * Outputs: 3-channel pseudo-image (amplitude, phase, temporal diff)
+ * matching the ADR-018 frame format expectations.
+ */
+
+export class CsiSimulator {
+  constructor(opts = {}) {
+    this.subcarriers = opts.subcarriers || 52; // 802.11n HT20
+    this.timeWindow = opts.timeWindow || 56;   // frames in sliding window
+    this.mode = 'demo'; // 'demo' | 'live'
+    this.ws = null;
+
+    // Circular buffer for CSI frames
+    this.amplitudeBuffer = [];
+    this.phaseBuffer = [];
+    this.frameCount = 0;
+
+    // Noise parameters
+    this._rng = this._mulberry32(opts.seed || 7);
+    this._noiseState = new Float32Array(this.subcarriers);
+    this._baseAmplitude = new Float32Array(this.subcarriers);
+    this._basePhase = new Float32Array(this.subcarriers);
+
+    // Initialize base CSI profile (empty room)
+    for (let i = 0; i < this.subcarriers; i++) {
+      this._baseAmplitude[i] = 0.5 + 0.3 * Math.sin(i * 0.12);
+      this._basePhase[i] = (i / this.subcarriers) * Math.PI * 2;
+    }
+
+    // Person influence (updated from video motion)
+    this.personPresence = 0;
+    this.personX = 0.5;
+    this.personY = 0.5;
+    this.personMotion = 0;
+  }
+
+  /**
+   * Connect to live sensing server WebSocket
+   * @param {string} url - WebSocket URL (e.g. ws://localhost:3030/ws/csi)
+   */
+  async connectLive(url) {
+    return new Promise((resolve) => {
+      try {
+        this.ws = new WebSocket(url);
+        this.ws.binaryType = 'arraybuffer';
+        this.ws.onmessage = (evt) => this._handleLiveFrame(evt.data);
+        this.ws.onopen = () => { this.mode = 'live'; resolve(true); };
+        this.ws.onerror = () => resolve(false);
+        this.ws.onclose = () => { this.mode = 'demo'; };
+        // Timeout after 3s
+        setTimeout(() => { if (this.mode !== 'live') resolve(false); }, 3000);
+      } catch {
+        resolve(false);
+      }
+    });
+  }
+
+  disconnect() {
+    if (this.ws) { this.ws.close(); this.ws = null; }
+    this.mode = 'demo';
+  }
+
+  get isLive() { return this.mode === 'live'; }
+
+  /**
+   * Update person state from video detection (for correlated demo data).
+   * When person exits frame, CSI maintains presence with slow decay
+   * (simulating through-wall sensing capability).
+   */
+  updatePersonState(presence, x, y, motion) {
+    if (presence > 0.1) {
+      // Person detected in video — update CSI state directly
+      this.personPresence = presence;
+      this.personX = x;
+      this.personY = y;
+      this.personMotion = motion;
+      this._lastSeenTime = performance.now();
+      this._lastSeenX = x;
+      this._lastSeenY = y;
+    } else if (this._lastSeenTime) {
+      // Person NOT in video — CSI "through-wall" persistence
+      const elapsed = (performance.now() - this._lastSeenTime) / 1000;
+      // CSI can sense through walls for ~10 seconds with decaying confidence
+      const decayRate = 0.15; // Lose ~15% per second
+      this.personPresence = Math.max(0, 1.0 - elapsed * decayRate);
+      // Position slowly drifts (person walking behind wall)
+      this.personX = this._lastSeenX;
+      this.personY = this._lastSeenY;
+      this.personMotion = Math.max(0, motion * 0.5 + this.personPresence * 0.2);
+
+      if (this.personPresence < 0.05) {
+        this._lastSeenTime = null;
+      }
+    } else {
+      this.personPresence = 0;
+      this.personMotion = 0;
+    }
+  }
+
+  /**
+   * Generate next CSI frame (demo mode) or return latest live frame
+   * @param {number} elapsed - Time in seconds
+   * @returns {{ amplitude: Float32Array, phase: Float32Array, snr: number }}
+   */
+  nextFrame(elapsed) {
+    const amp = new Float32Array(this.subcarriers);
+    const phase = new Float32Array(this.subcarriers);
+
+    if (this.mode === 'live' && this._liveAmplitude) {
+      amp.set(this._liveAmplitude);
+      phase.set(this._livePhase);
+    } else {
+      this._generateDemoFrame(amp, phase, elapsed);
+    }
+
+    // Push to circular buffer
+    this.amplitudeBuffer.push(new Float32Array(amp));
+    this.phaseBuffer.push(new Float32Array(phase));
+    if (this.amplitudeBuffer.length > this.timeWindow) {
+      this.amplitudeBuffer.shift();
+      this.phaseBuffer.shift();
+    }
+
+    // SNR estimate
+    let signalPower = 0, noisePower = 0;
+    for (let i = 0; i < this.subcarriers; i++) {
+      signalPower += amp[i] * amp[i];
+      noisePower += this._noiseState[i] * this._noiseState[i];
+    }
+    const snr = noisePower > 0 ? 10 * Math.log10(signalPower / noisePower) : 30;
+
+    this.frameCount++;
+    return { amplitude: amp, phase, snr: Math.max(0, Math.min(40, snr)) };
+  }
+
+  /**
+   * Build 3-channel pseudo-image for CNN input
+   * @param {number} targetSize - Output image dimension (square)
+   * @returns {Uint8Array} RGB data (targetSize * targetSize * 3)
+   */
+  buildPseudoImage(targetSize = 56) {
+    const buf = this.amplitudeBuffer;
+    const pBuf = this.phaseBuffer;
+    const frames = buf.length;
+    if (frames < 2) {
+      return new Uint8Array(targetSize * targetSize * 3);
+    }
+
+    const rgb = new Uint8Array(targetSize * targetSize * 3);
+
+    for (let y = 0; y < targetSize; y++) {
+      const fi = Math.min(Math.floor(y / targetSize * frames), frames - 1);
+      for (let x = 0; x < targetSize; x++) {
+        const si = Math.min(Math.floor(x / targetSize * this.subcarriers), this.subcarriers - 1);
+        const idx = (y * targetSize + x) * 3;
+
+        // R: Amplitude (normalized to 0-255)
+        const ampVal = buf[fi][si];
+        rgb[idx] = Math.min(255, Math.max(0, Math.floor(ampVal * 255)));
+
+        // G: Phase (wrapped to 0-255)
+        const phaseVal = (pBuf[fi][si] % (2 * Math.PI) + 2 * Math.PI) % (2 * Math.PI);
+        rgb[idx + 1] = Math.floor(phaseVal / (2 * Math.PI) * 255);
+
+        // B: Temporal difference
+        if (fi > 0) {
+          const diff = Math.abs(buf[fi][si] - buf[fi - 1][si]);
+          rgb[idx + 2] = Math.min(255, Math.floor(diff * 500));
+        }
+      }
+    }
+
+    return rgb;
+  }
+
+  /**
+   * Get heatmap data for visualization
+   * @returns {{ data: Float32Array, width: number, height: number }}
+   */
+  getHeatmapData() {
+    const frames = this.amplitudeBuffer.length;
+    const w = this.subcarriers;
+    const h = Math.min(frames, this.timeWindow);
+    const data = new Float32Array(w * h);
+    for (let y = 0; y < h; y++) {
+      const fi = frames - h + y;
+      if (fi >= 0 && fi < frames) {
+        for (let x = 0; x < w; x++) {
+          data[y * w + x] = this.amplitudeBuffer[fi][x];
+        }
+      }
+    }
+    return { data, width: w, height: h };
+  }
+
+  // === Private ===
+
+  _generateDemoFrame(amp, phase, elapsed) {
+    const rng = this._rng;
+    const presence = this.personPresence;
+    const motion = this.personMotion;
+    const px = this.personX;
+
+    for (let i = 0; i < this.subcarriers; i++) {
+      // Base CSI profile (frequency-selective channel)
+      let a = this._baseAmplitude[i];
+      let p = this._basePhase[i] + elapsed * 0.05;
+
+      // Environmental noise (correlated across subcarriers)
+      this._noiseState[i] = 0.95 * this._noiseState[i] + 0.05 * (rng() * 2 - 1) * 0.03;
+      a += this._noiseState[i];
+
+      // Person-induced CSI perturbation
+      if (presence > 0.1) {
+        // Subcarrier-dependent body reflection (Fresnel zone model)
+        const freqOffset = (i - this.subcarriers * px) / (this.subcarriers * 0.3);
+        const bodyReflection = presence * 0.25 * Math.exp(-freqOffset * freqOffset);
+
+        // Motion causes amplitude fluctuation
+        const motionEffect = motion * 0.15 * Math.sin(elapsed * 3.5 + i * 0.3);
+
+        // Breathing modulation (0.2-0.3 Hz)
+        const breathing = presence * 0.02 * Math.sin(elapsed * 1.5 + i * 0.05);
+
+        a += bodyReflection + motionEffect + breathing;
+        p += presence * 0.4 * Math.sin(elapsed * 2.1 + i * 0.15);
+      }
+
+      amp[i] = Math.max(0, Math.min(1, a));
+      phase[i] = p;
+    }
+  }
+
+  _handleLiveFrame(data) {
+    const view = new DataView(data);
+    // Check ADR-018 magic: 0xC5110001
+    if (data.byteLength < 20) return;
+    const magic = view.getUint32(0, true);
+    if (magic !== 0xC5110001) return;
+
+    const numSub = Math.min(view.getUint16(8, true), this.subcarriers);
+    this._liveAmplitude = new Float32Array(this.subcarriers);
+    this._livePhase = new Float32Array(this.subcarriers);
+
+    const headerSize = 20;
+    for (let i = 0; i < numSub && (headerSize + i * 4 + 3) < data.byteLength; i++) {
+      const real = view.getInt16(headerSize + i * 4, true);
+      const imag = view.getInt16(headerSize + i * 4 + 2, true);
+      this._liveAmplitude[i] = Math.sqrt(real * real + imag * imag) / 2048;
+      this._livePhase[i] = Math.atan2(imag, real);
+    }
+  }
+
+  _mulberry32(seed) {
+    return function() {
+      let t = (seed += 0x6D2B79F5);
+      t = Math.imul(t ^ (t >>> 15), t | 1);
+      t ^= t + Math.imul(t ^ (t >>> 7), t | 61);
+      return ((t ^ (t >>> 14)) >>> 0) / 4294967296;
+    };
+  }
+}
@@ -0,0 +1,166 @@
+/**
+ * FusionEngine — Attention-weighted dual-modal embedding fusion.
+ *
+ * Combines visual (camera) and CSI (WiFi) embeddings with dynamic
+ * confidence gating based on signal quality.
+ */
+
+export class FusionEngine {
+  /**
+   * @param {number} embeddingDim
+   */
+  constructor(embeddingDim = 128) {
+    this.embeddingDim = embeddingDim;
+
+    // Learnable attention weights (initialized to balanced 0.5)
+    // In production, these would be loaded from trained JSON
+    this.attentionWeights = new Float32Array(embeddingDim).fill(0.5);
+
+    // Dynamic modality confidence [0, 1]
+    this.videoConfidence = 1.0;
+    this.csiConfidence = 0.0;
+    this.fusedConfidence = 0.5;
+
+    // Smoothing for confidence transitions
+    this._smoothAlpha = 0.85;
+
+    // Embedding history for visualization
+    this.recentVideoEmbeddings = [];
+    this.recentCsiEmbeddings = [];
+    this.recentFusedEmbeddings = [];
+    this.maxHistory = 50;
+  }
+
+  /**
+   * Update quality-based confidence scores
+   * @param {number} videoBrightness - [0,1] video brightness quality
+   * @param {number} videoMotion     - [0,1] motion detected
+   * @param {number} csiSnr          - CSI signal-to-noise ratio in dB
+   * @param {boolean} csiActive      - Whether CSI source is connected
+   */
+  updateConfidence(videoBrightness, videoMotion, csiSnr, csiActive) {
+    // Video confidence: drops with low brightness, boosted by motion
+    let vc = 0;
+    if (videoBrightness > 0.05) {
+      vc = Math.min(1, videoBrightness * 1.5) * 0.7 + Math.min(1, videoMotion * 3) * 0.3;
+    }
+
+    // CSI confidence: based on SNR and connection status
+    let cc = 0;
+    if (csiActive) {
+      cc = Math.min(1, csiSnr / 25); // 25dB = full confidence
+    }
+
+    // Smooth transitions
+    this.videoConfidence = this._smoothAlpha * this.videoConfidence + (1 - this._smoothAlpha) * vc;
+    this.csiConfidence = this._smoothAlpha * this.csiConfidence + (1 - this._smoothAlpha) * cc;
+
+    // Fused confidence is the max of either (fusion can only help)
+    this.fusedConfidence = Math.min(1, Math.sqrt(
+      this.videoConfidence * this.videoConfidence + this.csiConfidence * this.csiConfidence
+    ));
+  }
+
+  /**
+   * Fuse video and CSI embeddings
+   * @param {Float32Array|null} videoEmb - Visual embedding (or null if video-off)
+   * @param {Float32Array|null} csiEmb   - CSI embedding (or null if CSI-off)
+   * @param {string} mode                - 'dual' | 'video' | 'csi'
+   * @returns {Float32Array} Fused embedding
+   */
+  fuse(videoEmb, csiEmb, mode = 'dual') {
+    const dim = this.embeddingDim;
+    const fused = new Float32Array(dim);
+
+    if (mode === 'video' || !csiEmb) {
+      if (videoEmb) fused.set(videoEmb);
+      this._recordEmbedding(videoEmb, null, fused);
+      return fused;
+    }
+
+    if (mode === 'csi' || !videoEmb) {
+      if (csiEmb) fused.set(csiEmb);
+      this._recordEmbedding(null, csiEmb, fused);
+      return fused;
+    }
+
+    // Dual mode: attention-weighted fusion with confidence gating
+    const totalConf = this.videoConfidence + this.csiConfidence;
+    const videoWeight = totalConf > 0 ? this.videoConfidence / totalConf : 0.5;
+
+    for (let i = 0; i < dim; i++) {
+      const alpha = this.attentionWeights[i] * videoWeight +
+                    (1 - this.attentionWeights[i]) * (1 - videoWeight);
+      fused[i] = alpha * videoEmb[i] + (1 - alpha) * csiEmb[i];
+    }
+
+    // Re-normalize
+    let norm = 0;
+    for (let i = 0; i < dim; i++) norm += fused[i] * fused[i];
+    norm = Math.sqrt(norm);
+    if (norm > 1e-8) {
+      for (let i = 0; i < dim; i++) fused[i] /= norm;
+    }
+
+    this._recordEmbedding(videoEmb, csiEmb, fused);
+    return fused;
+  }
+
+  /**
+   * Get embedding pairs for 2D visualization (PCA projection)
+   * @returns {{ video: Array, csi: Array, fused: Array }}
+   */
+  getEmbeddingPoints() {
+    // Simple 2D projection using first two principal components (approximated)
+    const project = (emb) => {
+      if (!emb || emb.length < 4) return null;
+      // Use pairs of dimensions as crude 2D projection
+      let x = 0, y = 0;
+      for (let i = 0; i < emb.length; i += 2) {
+        x += emb[i] * (i % 4 < 2 ? 1 : -1);
+        if (i + 1 < emb.length) {
+          y += emb[i + 1] * (i % 4 < 2 ? 1 : -1);
+        }
+      }
+      return [x * 2, y * 2]; // Scale for visibility
+    };
+
+    return {
+      video: this.recentVideoEmbeddings.map(project).filter(Boolean),
+      csi: this.recentCsiEmbeddings.map(project).filter(Boolean),
+      fused: this.recentFusedEmbeddings.map(project).filter(Boolean)
+    };
+  }
+
+  /**
+   * Cross-modal similarity score
+   * @returns {number} Cosine similarity between latest video and CSI embeddings
+   */
+  getCrossModalSimilarity() {
+    const v = this.recentVideoEmbeddings[this.recentVideoEmbeddings.length - 1];
+    const c = this.recentCsiEmbeddings[this.recentCsiEmbeddings.length - 1];
+    if (!v || !c) return 0;
+
+    let dot = 0, na = 0, nb = 0;
+    for (let i = 0; i < v.length; i++) {
+      dot += v[i] * c[i];
+      na += v[i] * v[i];
+      nb += c[i] * c[i];
+    }
+    na = Math.sqrt(na); nb = Math.sqrt(nb);
+    return (na > 1e-8 && nb > 1e-8) ? dot / (na * nb) : 0;
+  }
+
+  _recordEmbedding(video, csi, fused) {
+    if (video) {
+      this.recentVideoEmbeddings.push(new Float32Array(video));
+      if (this.recentVideoEmbeddings.length > this.maxHistory) this.recentVideoEmbeddings.shift();
+    }
+    if (csi) {
+      this.recentCsiEmbeddings.push(new Float32Array(csi));
+      if (this.recentCsiEmbeddings.length > this.maxHistory) this.recentCsiEmbeddings.shift();
+    }
+    this.recentFusedEmbeddings.push(new Float32Array(fused));
+    if (this.recentFusedEmbeddings.length > this.maxHistory) this.recentFusedEmbeddings.shift();
+  }
+}
@@ -0,0 +1,315 @@
+/**
+ * WiFi-DensePose — Dual-Modal Pose Estimation Demo
+ *
+ * Main orchestration: video capture → CNN embedding → CSI processing → fusion → rendering
+ */
+
+import { VideoCapture } from './video-capture.js';
+import { CsiSimulator } from './csi-simulator.js';
+import { CnnEmbedder } from './cnn-embedder.js';
+import { FusionEngine } from './fusion-engine.js';
+import { PoseDecoder } from './pose-decoder.js';
+import { CanvasRenderer } from './canvas-renderer.js';
+
+// === State ===
+let mode = 'dual';  // 'dual' | 'video' | 'csi'
+let isRunning = false;
+let isPaused = false;
+let startTime = 0;
+let frameCount = 0;
+let fps = 0;
+let lastFpsTime = 0;
+let confidenceThreshold = 0.3;
+
+// Latency tracking
+const latency = { video: 0, csi: 0, fusion: 0, total: 0 };
+
+// === Components ===
+const videoCapture = new VideoCapture(document.getElementById('webcam'));
+const csiSimulator = new CsiSimulator({ subcarriers: 52, timeWindow: 56 });
+const visualCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 42 });
+const csiCnn = new CnnEmbedder({ inputSize: 56, embeddingDim: 128, seed: 137 });
+const fusionEngine = new FusionEngine(128);
+const poseDecoder = new PoseDecoder(128);
+const renderer = new CanvasRenderer();
+
+// === Canvas Elements ===
+const skeletonCanvas = document.getElementById('skeleton-canvas');
+const skeletonCtx = skeletonCanvas.getContext('2d');
+const csiCanvas = document.getElementById('csi-canvas');
+const csiCtx = csiCanvas.getContext('2d');
+const embeddingCanvas = document.getElementById('embedding-canvas');
+const embeddingCtx = embeddingCanvas.getContext('2d');
+
+// === UI Elements ===
+const modeSelect = document.getElementById('mode-select');
+const statusDot = document.getElementById('status-dot');
+const statusLabel = document.getElementById('status-label');
+const fpsDisplay = document.getElementById('fps-display');
+const cameraPrompt = document.getElementById('camera-prompt');
+const startCameraBtn = document.getElementById('start-camera-btn');
+const pauseBtn = document.getElementById('pause-btn');
+const confSlider = document.getElementById('confidence-slider');
+const confValue = document.getElementById('confidence-value');
+const wsUrlInput = document.getElementById('ws-url');
+const connectWsBtn = document.getElementById('connect-ws-btn');
+
+// Fusion bar elements
+const videoBar = document.getElementById('video-bar');
+const csiBar = document.getElementById('csi-bar');
+const fusedBar = document.getElementById('fused-bar');
+const videoBarVal = document.getElementById('video-bar-val');
+const csiBarVal = document.getElementById('csi-bar-val');
+const fusedBarVal = document.getElementById('fused-bar-val');
+
+// Latency elements
+const latVideoEl = document.getElementById('lat-video');
+const latCsiEl = document.getElementById('lat-csi');
+const latFusionEl = document.getElementById('lat-fusion');
+const latTotalEl = document.getElementById('lat-total');
+
+// Cross-modal similarity
+const crossModalEl = document.getElementById('cross-modal-sim');
+
+// === Initialize ===
+function init() {
+  resizeCanvases();
+  window.addEventListener('resize', resizeCanvases);
+
+  // Mode change
+  modeSelect.addEventListener('change', (e) => {
+    mode = e.target.value;
+    updateModeUI();
+  });
+
+  // Camera start
+  startCameraBtn.addEventListener('click', startCamera);
+
+  // Pause
+  pauseBtn.addEventListener('click', () => {
+    isPaused = !isPaused;
+    pauseBtn.textContent = isPaused ? '▶ Resume' : '⏸ Pause';
+    pauseBtn.classList.toggle('active', isPaused);
+  });
+
+  // Confidence slider
+  confSlider.addEventListener('input', (e) => {
+    confidenceThreshold = parseFloat(e.target.value);
+    confValue.textContent = confidenceThreshold.toFixed(2);
+  });
+
+  // WebSocket connect
+  connectWsBtn.addEventListener('click', async () => {
+    const url = wsUrlInput.value.trim();
+    if (!url) return;
+    connectWsBtn.textContent = 'Connecting...';
+    const ok = await csiSimulator.connectLive(url);
+    connectWsBtn.textContent = ok ? '✓ Connected' : 'Connect';
+    if (ok) {
+      connectWsBtn.classList.add('active');
+    }
+  });
+
+  // Try to load WASM embedders (non-blocking)
+  // Resolve relative to this JS module file (in pose-fusion/js/) → ../pkg/
+  const wasmBase = new URL('../pkg/ruvector_cnn_wasm', import.meta.url).href;
+  visualCnn.tryLoadWasm(wasmBase);
+  csiCnn.tryLoadWasm(wasmBase);
+
+  // Auto-connect to local sensing server WebSocket if available
+  const defaultWsUrl = 'ws://localhost:8765/ws/sensing';
+  if (wsUrlInput) wsUrlInput.value = defaultWsUrl;
+  csiSimulator.connectLive(defaultWsUrl).then(ok => {
+    if (ok && connectWsBtn) {
+      connectWsBtn.textContent = '✓ Live ESP32';
+      connectWsBtn.classList.add('active');
+      statusLabel.textContent = 'LIVE CSI';
+      statusDot.classList.remove('offline');
+    }
+  });
+
+  // Auto-start camera for video/dual modes
+  updateModeUI();
+  startTime = performance.now() / 1000;
+  isRunning = true;
+  requestAnimationFrame(mainLoop);
+}
+
+async function startCamera() {
+  cameraPrompt.style.display = 'none';
+  const ok = await videoCapture.start();
+  if (ok) {
+    statusDot.classList.remove('offline');
+    statusLabel.textContent = 'LIVE';
+    resizeCanvases();
+  } else {
+    cameraPrompt.style.display = 'flex';
+    cameraPrompt.querySelector('p').textContent = 'Camera access denied. Try CSI-only mode.';
+  }
+}
+
+function updateModeUI() {
+  const needsVideo = mode !== 'csi';
+  const needsCsi = mode !== 'video';
+
+  // Show/hide camera prompt
+  if (needsVideo && !videoCapture.isActive) {
+    cameraPrompt.style.display = 'flex';
+  } else {
+    cameraPrompt.style.display = 'none';
+  }
+}
+
+function resizeCanvases() {
+  const videoPanel = document.querySelector('.video-panel');
+  if (videoPanel) {
+    const rect = videoPanel.getBoundingClientRect();
+    skeletonCanvas.width = rect.width;
+    skeletonCanvas.height = rect.height;
+  }
+
+  // CSI canvas
+  csiCanvas.width = csiCanvas.parentElement.clientWidth;
+  csiCanvas.height = 120;
+
+  // Embedding canvas
+  embeddingCanvas.width = embeddingCanvas.parentElement.clientWidth;
+  embeddingCanvas.height = 140;
+}
+
+// === Main Loop ===
+function mainLoop(timestamp) {
+  if (!isRunning) return;
+  requestAnimationFrame(mainLoop);
+
+  if (isPaused) return;
+
+  const elapsed = performance.now() / 1000 - startTime;
+  const totalStart = performance.now();
+
+  // --- Video Pipeline ---
+  let videoEmb = null;
+  let motionRegion = null;
+  if (mode !== 'csi' && videoCapture.isActive) {
+    const t0 = performance.now();
+    const frame = videoCapture.captureFrame(56, 56);
+    if (frame) {
+      videoEmb = visualCnn.extract(frame.rgb, frame.width, frame.height);
+      motionRegion = videoCapture.detectMotionRegion(56, 56);
+
+      // Feed motion to CSI simulator for correlated demo data
+      // When detected=false, CSI simulator handles through-wall persistence
+      csiSimulator.updatePersonState(
+        motionRegion.detected ? 1.0 : 0,
+        motionRegion.detected ? motionRegion.x + motionRegion.w / 2 : 0.5,
+        motionRegion.detected ? motionRegion.y + motionRegion.h / 2 : 0.5,
+        frame.motion
+      );
+
+      fusionEngine.updateConfidence(
+        frame.brightness, frame.motion,
+        0, csiSimulator.isLive || mode === 'dual'
+      );
+    }
+    latency.video = performance.now() - t0;
+  }
+
+  // --- CSI Pipeline ---
+  let csiEmb = null;
+  if (mode !== 'video') {
+    const t0 = performance.now();
+    const csiFrame = csiSimulator.nextFrame(elapsed);
+    const pseudoImage = csiSimulator.buildPseudoImage(56);
+    csiEmb = csiCnn.extract(pseudoImage, 56, 56);
+
+    fusionEngine.updateConfidence(
+      videoCapture.brightnessScore,
+      videoCapture.motionScore,
+      csiFrame.snr,
+      true
+    );
+
+    // Draw CSI heatmap
+    const heatmap = csiSimulator.getHeatmapData();
+    renderer.drawCsiHeatmap(csiCtx, heatmap, csiCanvas.width, csiCanvas.height);
+
+    latency.csi = performance.now() - t0;
+  }
+
+  // --- Fusion ---
+  const t0f = performance.now();
+  const fusedEmb = fusionEngine.fuse(videoEmb, csiEmb, mode);
+  latency.fusion = performance.now() - t0f;
+
+  // --- Pose Decode ---
+  // For CSI-only mode, generate a synthetic motion region from CSI energy
+  if (mode === 'csi' && (!motionRegion || !motionRegion.detected)) {
+    const csiPresence = csiSimulator.personPresence;
+    if (csiPresence > 0.1) {
+      motionRegion = {
+        detected: true,
+        x: 0.25, y: 0.15, w: 0.5, h: 0.7,
+        coverage: csiPresence,
+        motionGrid: null,
+        gridCols: 10,
+        gridRows: 8
+      };
+    }
+  }
+
+  // CSI state for through-wall tracking
+  const csiState = {
+    csiPresence: csiSimulator.personPresence,
+    isLive: csiSimulator.isLive
+  };
+
+  const keypoints = poseDecoder.decode(fusedEmb, motionRegion, elapsed, csiState);
+
+  // --- Render Skeleton ---
+  const labelMap = { dual: 'DUAL FUSION', video: 'VIDEO ONLY', csi: 'CSI ONLY' };
+  renderer.drawSkeleton(skeletonCtx, keypoints, skeletonCanvas.width, skeletonCanvas.height, {
+    minConfidence: confidenceThreshold,
+    color: mode === 'csi' ? 'amber' : 'green',
+    label: labelMap[mode]
+  });
+
+  // --- Render Embedding Space ---
+  const embPoints = fusionEngine.getEmbeddingPoints();
+  renderer.drawEmbeddingSpace(embeddingCtx, embPoints, embeddingCanvas.width, embeddingCanvas.height);
+
+  // --- Update UI ---
+  latency.total = performance.now() - totalStart;
+
+  // FPS
+  frameCount++;
+  if (timestamp - lastFpsTime > 500) {
+    fps = Math.round(frameCount * 1000 / (timestamp - lastFpsTime));
+    lastFpsTime = timestamp;
+    frameCount = 0;
+    fpsDisplay.textContent = `${fps} FPS`;
+  }
+
+  // Fusion bars
+  const vc = fusionEngine.videoConfidence;
+  const cc = fusionEngine.csiConfidence;
+  const fc = fusionEngine.fusedConfidence;
+  videoBar.style.width = `${vc * 100}%`;
+  csiBar.style.width = `${cc * 100}%`;
+  fusedBar.style.width = `${fc * 100}%`;
+  videoBarVal.textContent = `${Math.round(vc * 100)}%`;
+  csiBarVal.textContent = `${Math.round(cc * 100)}%`;
+  fusedBarVal.textContent = `${Math.round(fc * 100)}%`;
+
+  // Latency
+  latVideoEl.textContent = `${latency.video.toFixed(1)}ms`;
+  latCsiEl.textContent = `${latency.csi.toFixed(1)}ms`;
+  latFusionEl.textContent = `${latency.fusion.toFixed(1)}ms`;
+  latTotalEl.textContent = `${latency.total.toFixed(1)}ms`;
+
+  // Cross-modal similarity
+  const sim = fusionEngine.getCrossModalSimilarity();
+  crossModalEl.textContent = sim.toFixed(3);
+}
+
+// Boot
+document.addEventListener('DOMContentLoaded', init);
@@ -0,0 +1,373 @@
+/**
+ * PoseDecoder — Maps motion detection grid → 17 COCO keypoints.
+ *
+ * Uses per-cell motion intensity to track actual body part positions:
+ * - Head: top-center motion cluster
+ * - Shoulders/Elbows/Wrists: lateral motion in upper body zone
+ * - Hips/Knees/Ankles: lower body motion distribution
+ *
+ * When person exits frame, CSI data continues tracking (through-wall mode).
+ */
+
+// COCO keypoint definitions
+export const KEYPOINT_NAMES = [
+  'nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
+  'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
+  'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
+  'left_knee', 'right_knee', 'left_ankle', 'right_ankle'
+];
+
+// Skeleton connections (pairs of keypoint indices)
+export const SKELETON_CONNECTIONS = [
+  [0, 1], [0, 2], [1, 3], [2, 4],           // Head
+  [5, 6],                                     // Shoulders
+  [5, 7], [7, 9],                             // Left arm
+  [6, 8], [8, 10],                            // Right arm
+  [5, 11], [6, 12],                           // Torso
+  [11, 12],                                   // Hips
+  [11, 13], [13, 15],                         // Left leg
+  [12, 14], [14, 16],                         // Right leg
+];
+
+// Standard body proportions (relative to body height)
+const PROPORTIONS = {
+  headToShoulder: 0.15,
+  shoulderWidth: 0.25,
+  shoulderToElbow: 0.18,
+  elbowToWrist: 0.16,
+  shoulderToHip: 0.30,
+  hipWidth: 0.18,
+  hipToKnee: 0.24,
+  kneeToAnkle: 0.24,
+  eyeSpacing: 0.04,
+  earSpacing: 0.07,
+};
+
+export class PoseDecoder {
+  constructor(embeddingDim = 128) {
+    this.embeddingDim = embeddingDim;
+    this.smoothedKeypoints = null;
+    this.smoothingFactor = 0.45; // Lower = more responsive to movement
+    this._time = 0;
+
+    // Through-wall tracking state
+    this._lastBodyState = null;
+    this._ghostState = null;
+    this._ghostConfidence = 0;
+    this._ghostVelocity = { x: 0, y: 0 };
+
+    // Arm tracking history (smoothed positions)
+    this._leftArmY = 0.5;
+    this._rightArmY = 0.5;
+    this._leftArmX = 0;
+    this._rightArmX = 0;
+    this._headOffsetX = 0;
+  }
+
+  /**
+   * Decode motion data into 17 keypoints
+   * @param {Float32Array} embedding - Fused embedding vector
+   * @param {{ detected, x, y, w, h, motionGrid, gridCols, gridRows, motionCx, motionCy, exitDirection }} motionRegion
+   * @param {number} elapsed - Time in seconds
+   * @param {{ csiPresence: number }} csiState - CSI sensing state for through-wall
+   * @returns {Array<{x: number, y: number, confidence: number, name: string}>}
+   */
+  decode(embedding, motionRegion, elapsed, csiState = {}) {
+    this._time = elapsed;
+
+    const hasMotion = motionRegion && motionRegion.detected;
+    const hasCsi = csiState && csiState.csiPresence > 0.1;
+
+    if (hasMotion) {
+      // Active tracking from video motion grid
+      this._ghostConfidence = 0;
+      const rawKeypoints = this._trackFromMotionGrid(motionRegion, embedding, elapsed);
+      this._lastBodyState = { keypoints: rawKeypoints.map(kp => ({...kp})), time: elapsed };
+
+      // Track exit velocity
+      if (motionRegion.exitDirection) {
+        const speed = 0.008;
+        this._ghostVelocity = {
+          x: motionRegion.exitDirection === 'left' ? -speed : motionRegion.exitDirection === 'right' ? speed : 0,
+          y: motionRegion.exitDirection === 'up' ? -speed : motionRegion.exitDirection === 'down' ? speed : 0
+        };
+      }
+
+      // Apply temporal smoothing
+      if (this.smoothedKeypoints && this.smoothedKeypoints.length === rawKeypoints.length) {
+        const alpha = this.smoothingFactor;
+        for (let i = 0; i < rawKeypoints.length; i++) {
+          rawKeypoints[i].x = alpha * this.smoothedKeypoints[i].x + (1 - alpha) * rawKeypoints[i].x;
+          rawKeypoints[i].y = alpha * this.smoothedKeypoints[i].y + (1 - alpha) * rawKeypoints[i].y;
+        }
+      }
+
+      this.smoothedKeypoints = rawKeypoints;
+      return rawKeypoints;
+
+    } else if (this._lastBodyState && (hasCsi || this._ghostConfidence > 0.05)) {
+      // Through-wall mode: person left frame but CSI still senses them
+      return this._trackThroughWall(elapsed, csiState);
+
+    } else if (this.smoothedKeypoints) {
+      // Fade out
+      const faded = this.smoothedKeypoints.map(kp => ({
+        ...kp,
+        confidence: kp.confidence * 0.88
+      })).filter(kp => kp.confidence > 0.05);
+      if (faded.length === 0) this.smoothedKeypoints = null;
+      else this.smoothedKeypoints = faded;
+      return faded;
+    }
+
+    return [];
+  }
+
+  /**
+   * Track body parts from the motion grid.
+   * The grid tells us WHERE motion is happening → we map that to joint positions.
+   */
+  _trackFromMotionGrid(region, embedding, elapsed) {
+    const grid = region.motionGrid;
+    const cols = region.gridCols || 10;
+    const rows = region.gridRows || 8;
+
+    // Body bounding box
+    const cx = region.x + region.w / 2;
+    const cy = region.y + region.h / 2;
+    const bodyH = Math.max(region.h, 0.3);
+    const bodyW = Math.max(region.w, 0.15);
+
+    // Analyze the motion grid to find arm positions
+    // Divide body into zones: head (top 20%), arms (top 60% sides), torso (center), legs (bottom 40%)
+    if (grid) {
+      const armAnalysis = this._analyzeArmMotion(grid, cols, rows, region);
+      // Smooth arm tracking
+      this._leftArmY = 0.6 * this._leftArmY + 0.4 * armAnalysis.leftArmHeight;
+      this._rightArmY = 0.6 * this._rightArmY + 0.4 * armAnalysis.rightArmHeight;
+      this._leftArmX = 0.6 * this._leftArmX + 0.4 * armAnalysis.leftArmSpread;
+      this._rightArmX = 0.6 * this._rightArmX + 0.4 * armAnalysis.rightArmSpread;
+      this._headOffsetX = 0.7 * this._headOffsetX + 0.3 * armAnalysis.headOffsetX;
+    }
+
+    const P = PROPORTIONS;
+    const halfW = P.shoulderWidth * bodyH / 2;
+    const hipHalfW = P.hipWidth * bodyH / 2;
+
+    // Breathing (subtle)
+    const breathe = Math.sin(elapsed * 1.5) * 0.002;
+
+    // Core body positions from detection center
+    const hipY = cy + bodyH * 0.15;
+    const shoulderY = hipY - P.shoulderToHip * bodyH + breathe;
+    const headY = shoulderY - P.headToShoulder * bodyH;
+    const kneeY = hipY + P.hipToKnee * bodyH;
+    const ankleY = kneeY + P.kneeToAnkle * bodyH;
+
+    // HEAD follows motion centroid
+    const headX = cx + this._headOffsetX * bodyW * 0.3;
+
+    // ARM POSITIONS driven by motion grid analysis
+    // leftArmY: 0 = arm down at side, 1 = arm fully raised
+    // leftArmSpread: how far out the arm extends
+    const leftArmRaise = this._leftArmY;  // 0-1
+    const rightArmRaise = this._rightArmY;
+    const leftSpread = 0.02 + this._leftArmX * 0.12;
+    const rightSpread = 0.02 + this._rightArmX * 0.12;
+
+    // Elbow: interpolate between "at side" and "raised"
+    const lElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - leftArmRaise * 0.9);
+    const rElbowY = shoulderY + P.shoulderToElbow * bodyH * (1 - rightArmRaise * 0.9);
+    const lElbowX = cx - halfW - leftSpread;
+    const rElbowX = cx + halfW + rightSpread;
+
+    // Wrist: extends further when raised
+    const lWristY = lElbowY + P.elbowToWrist * bodyH * (1 - leftArmRaise * 1.1);
+    const rWristY = rElbowY + P.elbowToWrist * bodyH * (1 - rightArmRaise * 1.1);
+    const lWristX = lElbowX - leftSpread * 0.6;
+    const rWristX = rElbowX + rightSpread * 0.6;
+
+    // Leg motion from lower grid cells
+    const legMotion = grid ? this._analyzeLegMotion(grid, cols, rows) : { left: 0, right: 0 };
+    const legSwing = 0.015;
+
+    const keypoints = [
+      // 0: nose
+      { x: headX, y: headY + 0.01, confidence: 0.92 },
+      // 1: left_eye
+      { x: headX - P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
+      // 2: right_eye
+      { x: headX + P.eyeSpacing * bodyH, y: headY - 0.005, confidence: 0.88 },
+      // 3: left_ear
+      { x: headX - P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
+      // 4: right_ear
+      { x: headX + P.earSpacing * bodyH, y: headY + 0.005, confidence: 0.72 },
+      // 5: left_shoulder
+      { x: cx - halfW, y: shoulderY, confidence: 0.94 },
+      // 6: right_shoulder
+      { x: cx + halfW, y: shoulderY, confidence: 0.94 },
+      // 7: left_elbow
+      { x: lElbowX, y: lElbowY, confidence: 0.87 },
+      // 8: right_elbow
+      { x: rElbowX, y: rElbowY, confidence: 0.87 },
+      // 9: left_wrist
+      { x: lWristX, y: lWristY, confidence: 0.82 },
+      // 10: right_wrist
+      { x: rWristX, y: rWristY, confidence: 0.82 },
+      // 11: left_hip
+      { x: cx - hipHalfW, y: hipY, confidence: 0.91 },
+      // 12: right_hip
+      { x: cx + hipHalfW, y: hipY, confidence: 0.91 },
+      // 13: left_knee
+      { x: cx - hipHalfW + legMotion.left * legSwing, y: kneeY, confidence: 0.88 },
+      // 14: right_knee
+      { x: cx + hipHalfW + legMotion.right * legSwing, y: kneeY, confidence: 0.88 },
+      // 15: left_ankle
+      { x: cx - hipHalfW + legMotion.left * legSwing * 1.3, y: ankleY, confidence: 0.83 },
+      // 16: right_ankle
+      { x: cx + hipHalfW + legMotion.right * legSwing * 1.3, y: ankleY, confidence: 0.83 },
+    ];
+
+    for (let i = 0; i < keypoints.length; i++) {
+      keypoints[i].name = KEYPOINT_NAMES[i];
+    }
+
+    return keypoints;
+  }
+
+  /**
+   * Analyze the motion grid to determine arm positions.
+   * Left side of grid = left side of body, etc.
+   */
+  _analyzeArmMotion(grid, cols, rows, region) {
+    // Body center column
+    const centerCol = Math.floor(cols / 2);
+
+    // Upper body rows (top 60% of detected region)
+    const upperEnd = Math.floor(rows * 0.6);
+
+    // Compute motion intensity for left vs right, at different heights
+    let leftUpperMotion = 0, leftMidMotion = 0;
+    let rightUpperMotion = 0, rightMidMotion = 0;
+    let leftCount = 0, rightCount = 0;
+    let headMotionX = 0, headMotionWeight = 0;
+
+    for (let r = 0; r < upperEnd; r++) {
+      const heightWeight = 1.0 - (r / upperEnd) * 0.3; // Upper rows weighted more
+
+      // Head zone: top 25%, center 40% of width
+      if (r < Math.floor(rows * 0.25)) {
+        const headLeft = Math.floor(cols * 0.3);
+        const headRight = Math.floor(cols * 0.7);
+        for (let c = headLeft; c <= headRight; c++) {
+          const val = grid[r][c];
+          headMotionX += (c / cols - 0.5) * val;
+          headMotionWeight += val;
+        }
+      }
+
+      // Left arm zone: left 40% of grid
+      for (let c = 0; c < Math.floor(cols * 0.4); c++) {
+        const val = grid[r][c];
+        if (r < rows * 0.3) leftUpperMotion += val * heightWeight;
+        else leftMidMotion += val * heightWeight;
+        leftCount++;
+      }
+
+      // Right arm zone: right 40% of grid
+      for (let c = Math.floor(cols * 0.6); c < cols; c++) {
+        const val = grid[r][c];
+        if (r < rows * 0.3) rightUpperMotion += val * heightWeight;
+        else rightMidMotion += val * heightWeight;
+        rightCount++;
+      }
+    }
+
+    // Normalize
+    const leftTotal = leftUpperMotion + leftMidMotion;
+    const rightTotal = rightUpperMotion + rightMidMotion;
+    const maxMotion = 0.15; // Calibration threshold
+
+    // Arm height: 0 = at side, 1 = raised
+    // High motion in upper-left → left arm is raised
+    const leftArmHeight = Math.min(1, (leftUpperMotion / maxMotion) * 2);
+    const rightArmHeight = Math.min(1, (rightUpperMotion / maxMotion) * 2);
+
+    // Arm spread: how far out from body
+    const leftArmSpread = Math.min(1, leftTotal / maxMotion);
+    const rightArmSpread = Math.min(1, rightTotal / maxMotion);
+
+    // Head offset
+    const headOffsetX = headMotionWeight > 0.01 ? headMotionX / headMotionWeight : 0;
+
+    return { leftArmHeight, rightArmHeight, leftArmSpread, rightArmSpread, headOffsetX };
+  }
+
+  /**
+   * Analyze lower grid for leg motion.
+   */
+  _analyzeLegMotion(grid, cols, rows) {
+    const lowerStart = Math.floor(rows * 0.6);
+    let leftMotion = 0, rightMotion = 0;
+
+    for (let r = lowerStart; r < rows; r++) {
+      for (let c = 0; c < Math.floor(cols / 2); c++) {
+        leftMotion += grid[r][c];
+      }
+      for (let c = Math.floor(cols / 2); c < cols; c++) {
+        rightMotion += grid[r][c];
+      }
+    }
+
+    // Return as -1 to 1 range (asymmetry indicates which leg is moving)
+    const total = leftMotion + rightMotion + 0.001;
+    return {
+      left: (leftMotion - rightMotion) / total,
+      right: (rightMotion - leftMotion) / total
+    };
+  }
+
+  /**
+   * Through-wall tracking: continue showing pose via CSI when person left video frame.
+   * The skeleton drifts in the exit direction with decreasing confidence.
+   */
+  _trackThroughWall(elapsed, csiState) {
+    if (!this._lastBodyState) return [];
+
+    const dt = elapsed - this._lastBodyState.time;
+    const csiPresence = csiState.csiPresence || 0;
+
+    // Initialize ghost on first call
+    if (this._ghostConfidence <= 0.05) {
+      this._ghostConfidence = 0.8;
+      this._ghostState = this._lastBodyState.keypoints.map(kp => ({...kp}));
+    }
+
+    // Ghost confidence decays, but CSI presence sustains it
+    const csiBoost = Math.min(0.7, csiPresence * 0.8);
+    this._ghostConfidence = Math.max(0.05, this._ghostConfidence * 0.995 - 0.001 + csiBoost * 0.002);
+
+    // Drift the ghost in exit direction
+    const vx = this._ghostVelocity.x;
+    const vy = this._ghostVelocity.y;
+
+    // Breathing continues via CSI
+    const breathe = Math.sin(elapsed * 1.5) * 0.003 * csiPresence;
+
+    const keypoints = this._ghostState.map((kp, i) => {
+      return {
+        x: kp.x + vx * dt * 0.3,
+        y: kp.y + vy * dt * 0.3 + (i >= 5 && i <= 6 ? breathe : 0),
+        confidence: kp.confidence * this._ghostConfidence * (0.5 + csiPresence * 0.5),
+        name: kp.name
+      };
+    });
+
+    // Slow down drift over time
+    this._ghostVelocity.x *= 0.998;
+    this._ghostVelocity.y *= 0.998;
+
+    this.smoothedKeypoints = keypoints;
+    return keypoints;
+  }
+}
@@ -0,0 +1,235 @@
+/**
+ * VideoCapture — getUserMedia webcam capture with frame extraction.
+ * Provides quality metrics (brightness, motion) for fusion confidence gating.
+ */
+
+export class VideoCapture {
+  constructor(videoElement) {
+    this.video = videoElement;
+    this.stream = null;
+    this.offscreen = document.createElement('canvas');
+    this.offCtx = this.offscreen.getContext('2d', { willReadFrequently: true });
+    this.prevFrame = null;
+    this.motionScore = 0;
+    this.brightnessScore = 0;
+  }
+
+  async start(constraints = {}) {
+    const defaultConstraints = {
+      video: {
+        width: { ideal: 640 },
+        height: { ideal: 480 },
+        facingMode: 'user',
+        frameRate: { ideal: 30 }
+      },
+      audio: false
+    };
+
+    try {
+      this.stream = await navigator.mediaDevices.getUserMedia(
+        Object.keys(constraints).length ? constraints : defaultConstraints
+      );
+      this.video.srcObject = this.stream;
+      await this.video.play();
+
+      this.offscreen.width = this.video.videoWidth;
+      this.offscreen.height = this.video.videoHeight;
+
+      return true;
+    } catch (err) {
+      console.error('[Video] Camera access failed:', err.message);
+      return false;
+    }
+  }
+
+  stop() {
+    if (this.stream) {
+      this.stream.getTracks().forEach(t => t.stop());
+      this.stream = null;
+    }
+    this.video.srcObject = null;
+  }
+
+  get isActive() {
+    return this.stream !== null && this.video.readyState >= 2;
+  }
+
+  get width() { return this.video.videoWidth || 640; }
+  get height() { return this.video.videoHeight || 480; }
+
+  /**
+   * Capture current frame as RGB Uint8Array + compute quality metrics.
+   * @param {number} targetW - Target width for CNN input
+   * @param {number} targetH - Target height for CNN input
+   * @returns {{ rgb: Uint8Array, width: number, height: number, motion: number, brightness: number }}
+   */
+  captureFrame(targetW = 56, targetH = 56) {
+    if (!this.isActive) return null;
+
+    // Draw to offscreen at target resolution
+    this.offscreen.width = targetW;
+    this.offscreen.height = targetH;
+    this.offCtx.drawImage(this.video, 0, 0, targetW, targetH);
+    const imageData = this.offCtx.getImageData(0, 0, targetW, targetH);
+    const rgba = imageData.data;
+
+    // Convert RGBA → RGB
+    const pixels = targetW * targetH;
+    const rgb = new Uint8Array(pixels * 3);
+    let brightnessSum = 0;
+    let motionSum = 0;
+
+    for (let i = 0; i < pixels; i++) {
+      const r = rgba[i * 4];
+      const g = rgba[i * 4 + 1];
+      const b = rgba[i * 4 + 2];
+      rgb[i * 3] = r;
+      rgb[i * 3 + 1] = g;
+      rgb[i * 3 + 2] = b;
+
+      // Luminance for brightness
+      const lum = 0.299 * r + 0.587 * g + 0.114 * b;
+      brightnessSum += lum;
+
+      // Motion: diff from previous frame
+      if (this.prevFrame) {
+        const pr = this.prevFrame[i * 3];
+        const pg = this.prevFrame[i * 3 + 1];
+        const pb = this.prevFrame[i * 3 + 2];
+        motionSum += Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb);
+      }
+    }
+
+    this.brightnessScore = brightnessSum / (pixels * 255);
+    this.motionScore = this.prevFrame ? Math.min(1, motionSum / (pixels * 100)) : 0;
+    this.prevFrame = new Uint8Array(rgb);
+
+    return {
+      rgb,
+      width: targetW,
+      height: targetH,
+      motion: this.motionScore,
+      brightness: this.brightnessScore
+    };
+  }
+
+  /**
+   * Capture full-resolution RGBA for overlay rendering
+   * @returns {ImageData|null}
+   */
+  captureFullFrame() {
+    if (!this.isActive) return null;
+    this.offscreen.width = this.width;
+    this.offscreen.height = this.height;
+    this.offCtx.drawImage(this.video, 0, 0);
+    return this.offCtx.getImageData(0, 0, this.width, this.height);
+  }
+
+  /**
+   * Detect motion region + detailed motion grid for body-part tracking.
+   * Returns bounding box + a grid showing WHERE motion is concentrated.
+   * @returns {{ x, y, w, h, detected: boolean, motionGrid: number[][], gridCols: number, gridRows: number, exitDirection: string|null }}
+   */
+  detectMotionRegion(targetW = 56, targetH = 56) {
+    if (!this.isActive || !this.prevFrame) return { detected: false, motionGrid: null };
+
+    this.offscreen.width = targetW;
+    this.offscreen.height = targetH;
+    this.offCtx.drawImage(this.video, 0, 0, targetW, targetH);
+    const rgba = this.offCtx.getImageData(0, 0, targetW, targetH).data;
+
+    let minX = targetW, minY = targetH, maxX = 0, maxY = 0;
+    let motionPixels = 0;
+    const threshold = 25;
+
+    // Motion grid: divide frame into cells and track motion intensity per cell
+    const gridCols = 10;
+    const gridRows = 8;
+    const cellW = targetW / gridCols;
+    const cellH = targetH / gridRows;
+    const motionGrid = Array.from({ length: gridRows }, () => new Float32Array(gridCols));
+    const cellPixels = cellW * cellH;
+
+    // Also track motion centroid weighted by intensity
+    let motionCxSum = 0, motionCySum = 0, motionWeightSum = 0;
+
+    for (let y = 0; y < targetH; y++) {
+      for (let x = 0; x < targetW; x++) {
+        const i = y * targetW + x;
+        const r = rgba[i * 4], g = rgba[i * 4 + 1], b = rgba[i * 4 + 2];
+        const pr = this.prevFrame[i * 3], pg = this.prevFrame[i * 3 + 1], pb = this.prevFrame[i * 3 + 2];
+        const diff = Math.abs(r - pr) + Math.abs(g - pg) + Math.abs(b - pb);
+
+        if (diff > threshold * 3) {
+          motionPixels++;
+          if (x < minX) minX = x;
+          if (y < minY) minY = y;
+          if (x > maxX) maxX = x;
+          if (y > maxY) maxY = y;
+        }
+
+        // Accumulate per-cell motion intensity
+        const gc = Math.min(Math.floor(x / cellW), gridCols - 1);
+        const gr = Math.min(Math.floor(y / cellH), gridRows - 1);
+        const intensity = diff / (3 * 255); // Normalize 0-1
+        motionGrid[gr][gc] += intensity / cellPixels;
+
+        // Weighted centroid
+        if (diff > threshold) {
+          motionCxSum += x * diff;
+          motionCySum += y * diff;
+          motionWeightSum += diff;
+        }
+      }
+    }
+
+    const detected = motionPixels > (targetW * targetH * 0.02);
+
+    // Motion centroid (normalized 0-1)
+    const motionCx = motionWeightSum > 0 ? motionCxSum / (motionWeightSum * targetW) : 0.5;
+    const motionCy = motionWeightSum > 0 ? motionCySum / (motionWeightSum * targetH) : 0.5;
+
+    // Detect exit direction: if centroid is near edges
+    let exitDirection = null;
+    if (detected && motionCx < 0.1) exitDirection = 'left';
+    else if (detected && motionCx > 0.9) exitDirection = 'right';
+    else if (detected && motionCy < 0.1) exitDirection = 'up';
+    else if (detected && motionCy > 0.9) exitDirection = 'down';
+
+    // Track last known position for through-wall persistence
+    if (detected) {
+      this._lastDetected = {
+        x: minX / targetW,
+        y: minY / targetH,
+        w: (maxX - minX) / targetW,
+        h: (maxY - minY) / targetH,
+        cx: motionCx,
+        cy: motionCy,
+        exitDirection,
+        time: performance.now()
+      };
+    }
+
+    return {
+      detected,
+      x: minX / targetW,
+      y: minY / targetH,
+      w: (maxX - minX) / targetW,
+      h: (maxY - minY) / targetH,
+      coverage: motionPixels / (targetW * targetH),
+      motionGrid,
+      gridCols,
+      gridRows,
+      motionCx,
+      motionCy,
+      exitDirection
+    };
+  }
+
+  /**
+   * Get the last known detection info (for through-wall persistence)
+   */
+  get lastDetection() {
+    return this._lastDetected || null;
+  }
+}
@@ -0,0 +1,26 @@
+{
+  "name": "ruvector-cnn-wasm",
+  "type": "module",
+  "description": "WASM bindings for ruvector-cnn - CNN feature extraction for image embeddings",
+  "version": "0.1.0",
+  "license": "MIT OR Apache-2.0",
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/ruvnet/ruvector"
+  },
+  "files": [
+    "ruvector_cnn_wasm_bg.wasm",
+    "ruvector_cnn_wasm.js"
+  ],
+  "main": "ruvector_cnn_wasm.js",
+  "sideEffects": [
+    "./snippets/*"
+  ],
+  "keywords": [
+    "cnn",
+    "embeddings",
+    "wasm",
+    "simd",
+    "machine-learning"
+  ]
+}
@@ -0,0 +1,802 @@
+/**
+ * Configuration for CNN embedder
+ */
+export class EmbedderConfig {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        EmbedderConfigFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_embedderconfig_free(ptr, 0);
+    }
+    constructor() {
+        const ret = wasm.embedderconfig_new();
+        this.__wbg_ptr = ret >>> 0;
+        EmbedderConfigFinalization.register(this, this.__wbg_ptr, this);
+        return this;
+    }
+    /**
+     * Output embedding dimension
+     * @returns {number}
+     */
+    get embedding_dim() {
+        const ret = wasm.__wbg_get_embedderconfig_embedding_dim(this.__wbg_ptr);
+        return ret >>> 0;
+    }
+    /**
+     * Input image size (square)
+     * @returns {number}
+     */
+    get input_size() {
+        const ret = wasm.__wbg_get_embedderconfig_input_size(this.__wbg_ptr);
+        return ret >>> 0;
+    }
+    /**
+     * Whether to L2 normalize embeddings
+     * @returns {boolean}
+     */
+    get normalize() {
+        const ret = wasm.__wbg_get_embedderconfig_normalize(this.__wbg_ptr);
+        return ret !== 0;
+    }
+    /**
+     * Output embedding dimension
+     * @param {number} arg0
+     */
+    set embedding_dim(arg0) {
+        wasm.__wbg_set_embedderconfig_embedding_dim(this.__wbg_ptr, arg0);
+    }
+    /**
+     * Input image size (square)
+     * @param {number} arg0
+     */
+    set input_size(arg0) {
+        wasm.__wbg_set_embedderconfig_input_size(this.__wbg_ptr, arg0);
+    }
+    /**
+     * Whether to L2 normalize embeddings
+     * @param {boolean} arg0
+     */
+    set normalize(arg0) {
+        wasm.__wbg_set_embedderconfig_normalize(this.__wbg_ptr, arg0);
+    }
+}
+if (Symbol.dispose) EmbedderConfig.prototype[Symbol.dispose] = EmbedderConfig.prototype.free;
+
+/**
+ * Layer operations for building custom networks
+ */
+export class LayerOps {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        LayerOpsFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_layerops_free(ptr, 0);
+    }
+    /**
+     * Apply batch normalization (returns new array)
+     * @param {Float32Array} input
+     * @param {Float32Array} gamma
+     * @param {Float32Array} beta
+     * @param {Float32Array} mean
+     * @param {Float32Array} _var
+     * @param {number} epsilon
+     * @returns {Float32Array}
+     */
+    static batch_norm(input, gamma, beta, mean, _var, epsilon) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            const ptr1 = passArrayF32ToWasm0(gamma, wasm.__wbindgen_export2);
+            const len1 = WASM_VECTOR_LEN;
+            const ptr2 = passArrayF32ToWasm0(beta, wasm.__wbindgen_export2);
+            const len2 = WASM_VECTOR_LEN;
+            const ptr3 = passArrayF32ToWasm0(mean, wasm.__wbindgen_export2);
+            const len3 = WASM_VECTOR_LEN;
+            const ptr4 = passArrayF32ToWasm0(_var, wasm.__wbindgen_export2);
+            const len4 = WASM_VECTOR_LEN;
+            wasm.layerops_batch_norm(retptr, ptr0, len0, ptr1, len1, ptr2, len2, ptr3, len3, ptr4, len4, epsilon);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var v6 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v6;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Apply global average pooling
+     * Returns one value per channel
+     * @param {Float32Array} input
+     * @param {number} height
+     * @param {number} width
+     * @param {number} channels
+     * @returns {Float32Array}
+     */
+    static global_avg_pool(input, height, width, channels) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.layerops_global_avg_pool(retptr, ptr0, len0, height, width, channels);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var v2 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v2;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+}
+if (Symbol.dispose) LayerOps.prototype[Symbol.dispose] = LayerOps.prototype.free;
+
+/**
+ * SIMD-optimized operations
+ */
+export class SimdOps {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        SimdOpsFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_simdops_free(ptr, 0);
+    }
+    /**
+     * Dot product of two vectors
+     * @param {Float32Array} a
+     * @param {Float32Array} b
+     * @returns {number}
+     */
+    static dot_product(a, b) {
+        const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2);
+        const len0 = WASM_VECTOR_LEN;
+        const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2);
+        const len1 = WASM_VECTOR_LEN;
+        const ret = wasm.simdops_dot_product(ptr0, len0, ptr1, len1);
+        return ret;
+    }
+    /**
+     * L2 normalize a vector (returns new array)
+     * @param {Float32Array} data
+     * @returns {Float32Array}
+     */
+    static l2_normalize(data) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.simdops_l2_normalize(retptr, ptr0, len0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var v2 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v2;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * ReLU activation (returns new array)
+     * @param {Float32Array} data
+     * @returns {Float32Array}
+     */
+    static relu(data) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.simdops_relu(retptr, ptr0, len0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var v2 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v2;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * ReLU6 activation (returns new array)
+     * @param {Float32Array} data
+     * @returns {Float32Array}
+     */
+    static relu6(data) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(data, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.simdops_relu6(retptr, ptr0, len0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var v2 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v2;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+}
+if (Symbol.dispose) SimdOps.prototype[Symbol.dispose] = SimdOps.prototype.free;
+
+/**
+ * WASM CNN Embedder for image feature extraction
+ */
+export class WasmCnnEmbedder {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        WasmCnnEmbedderFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_wasmcnnembedder_free(ptr, 0);
+    }
+    /**
+     * Compute cosine similarity between two embeddings
+     * @param {Float32Array} a
+     * @param {Float32Array} b
+     * @returns {number}
+     */
+    cosine_similarity(a, b) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(a, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            const ptr1 = passArrayF32ToWasm0(b, wasm.__wbindgen_export2);
+            const len1 = WASM_VECTOR_LEN;
+            wasm.wasmcnnembedder_cosine_similarity(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1);
+            var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return r0;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Get the embedding dimension
+     * @returns {number}
+     */
+    get embedding_dim() {
+        const ret = wasm.wasmcnnembedder_embedding_dim(this.__wbg_ptr);
+        return ret >>> 0;
+    }
+    /**
+     * Extract embedding from image data (RGB format, row-major)
+     * @param {Uint8Array} image_data
+     * @param {number} width
+     * @param {number} height
+     * @returns {Float32Array}
+     */
+    extract(image_data, width, height) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArray8ToWasm0(image_data, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.wasmcnnembedder_extract(retptr, this.__wbg_ptr, ptr0, len0, width, height);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            var r3 = getDataViewMemory0().getInt32(retptr + 4 * 3, true);
+            if (r3) {
+                throw takeObject(r2);
+            }
+            var v2 = getArrayF32FromWasm0(r0, r1).slice();
+            wasm.__wbindgen_export(r0, r1 * 4, 4);
+            return v2;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Create a new CNN embedder
+     * @param {EmbedderConfig | null} [config]
+     */
+    constructor(config) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            let ptr0 = 0;
+            if (!isLikeNone(config)) {
+                _assertClass(config, EmbedderConfig);
+                ptr0 = config.__destroy_into_raw();
+            }
+            wasm.wasmcnnembedder_new(retptr, ptr0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            this.__wbg_ptr = r0 >>> 0;
+            WasmCnnEmbedderFinalization.register(this, this.__wbg_ptr, this);
+            return this;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+}
+if (Symbol.dispose) WasmCnnEmbedder.prototype[Symbol.dispose] = WasmCnnEmbedder.prototype.free;
+
+/**
+ * InfoNCE loss for contrastive learning (SimCLR style)
+ */
+export class WasmInfoNCELoss {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        WasmInfoNCELossFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_wasminfonceloss_free(ptr, 0);
+    }
+    /**
+     * Compute loss for a batch of embedding pairs
+     * embeddings: [2N, D] flattened where (i, i+N) are positive pairs
+     * @param {Float32Array} embeddings
+     * @param {number} batch_size
+     * @param {number} dim
+     * @returns {number}
+     */
+    forward(embeddings, batch_size, dim) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(embeddings, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.wasminfonceloss_forward(retptr, this.__wbg_ptr, ptr0, len0, batch_size, dim);
+            var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return r0;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Create new InfoNCE loss with temperature parameter
+     * @param {number} temperature
+     */
+    constructor(temperature) {
+        const ret = wasm.wasminfonceloss_new(temperature);
+        this.__wbg_ptr = ret >>> 0;
+        WasmInfoNCELossFinalization.register(this, this.__wbg_ptr, this);
+        return this;
+    }
+    /**
+     * Get the temperature parameter
+     * @returns {number}
+     */
+    get temperature() {
+        const ret = wasm.wasminfonceloss_temperature(this.__wbg_ptr);
+        return ret;
+    }
+}
+if (Symbol.dispose) WasmInfoNCELoss.prototype[Symbol.dispose] = WasmInfoNCELoss.prototype.free;
+
+/**
+ * Triplet loss for metric learning
+ */
+export class WasmTripletLoss {
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        WasmTripletLossFinalization.unregister(this);
+        return ptr;
+    }
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_wasmtripletloss_free(ptr, 0);
+    }
+    /**
+     * Compute loss for a batch of triplets
+     * @param {Float32Array} anchors
+     * @param {Float32Array} positives
+     * @param {Float32Array} negatives
+     * @param {number} dim
+     * @returns {number}
+     */
+    forward(anchors, positives, negatives, dim) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(anchors, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            const ptr1 = passArrayF32ToWasm0(positives, wasm.__wbindgen_export2);
+            const len1 = WASM_VECTOR_LEN;
+            const ptr2 = passArrayF32ToWasm0(negatives, wasm.__wbindgen_export2);
+            const len2 = WASM_VECTOR_LEN;
+            wasm.wasmtripletloss_forward(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2, dim);
+            var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return r0;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Compute loss for a single triplet
+     * @param {Float32Array} anchor
+     * @param {Float32Array} positive
+     * @param {Float32Array} negative
+     * @returns {number}
+     */
+    forward_single(anchor, positive, negative) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(anchor, wasm.__wbindgen_export2);
+            const len0 = WASM_VECTOR_LEN;
+            const ptr1 = passArrayF32ToWasm0(positive, wasm.__wbindgen_export2);
+            const len1 = WASM_VECTOR_LEN;
+            const ptr2 = passArrayF32ToWasm0(negative, wasm.__wbindgen_export2);
+            const len2 = WASM_VECTOR_LEN;
+            wasm.wasmtripletloss_forward_single(retptr, this.__wbg_ptr, ptr0, len0, ptr1, len1, ptr2, len2);
+            var r0 = getDataViewMemory0().getFloat32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return r0;
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Get the margin parameter
+     * @returns {number}
+     */
+    get margin() {
+        const ret = wasm.wasmtripletloss_margin(this.__wbg_ptr);
+        return ret;
+    }
+    /**
+     * Create new triplet loss with margin
+     * @param {number} margin
+     */
+    constructor(margin) {
+        const ret = wasm.wasmtripletloss_new(margin);
+        this.__wbg_ptr = ret >>> 0;
+        WasmTripletLossFinalization.register(this, this.__wbg_ptr, this);
+        return this;
+    }
+}
+if (Symbol.dispose) WasmTripletLoss.prototype[Symbol.dispose] = WasmTripletLoss.prototype.free;
+
+/**
+ * Initialize panic hook for better error messages
+ */
+export function init() {
+    wasm.init();
+}
+
+function __wbg_get_imports() {
+    const import0 = {
+        __proto__: null,
+        __wbg___wbindgen_throw_39bc967c0e5a9b58: function(arg0, arg1) {
+            throw new Error(getStringFromWasm0(arg0, arg1));
+        },
+        __wbg_error_a6fa202b58aa1cd3: function(arg0, arg1) {
+            let deferred0_0;
+            let deferred0_1;
+            try {
+                deferred0_0 = arg0;
+                deferred0_1 = arg1;
+                console.error(getStringFromWasm0(arg0, arg1));
+            } finally {
+                wasm.__wbindgen_export(deferred0_0, deferred0_1, 1);
+            }
+        },
+        __wbg_new_227d7c05414eb861: function() {
+            const ret = new Error();
+            return addHeapObject(ret);
+        },
+        __wbg_stack_3b0d974bbf31e44f: function(arg0, arg1) {
+            const ret = getObject(arg1).stack;
+            const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export2, wasm.__wbindgen_export3);
+            const len1 = WASM_VECTOR_LEN;
+            getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true);
+            getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true);
+        },
+        __wbindgen_cast_0000000000000001: function(arg0, arg1) {
+            // Cast intrinsic for `Ref(String) -> Externref`.
+            const ret = getStringFromWasm0(arg0, arg1);
+            return addHeapObject(ret);
+        },
+        __wbindgen_object_drop_ref: function(arg0) {
+            takeObject(arg0);
+        },
+    };
+    return {
+        __proto__: null,
+        "./ruvector_cnn_wasm_bg.js": import0,
+    };
+}
+
+const EmbedderConfigFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_embedderconfig_free(ptr >>> 0, 1));
+const LayerOpsFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_layerops_free(ptr >>> 0, 1));
+const SimdOpsFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_simdops_free(ptr >>> 0, 1));
+const WasmCnnEmbedderFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_wasmcnnembedder_free(ptr >>> 0, 1));
+const WasmInfoNCELossFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_wasminfonceloss_free(ptr >>> 0, 1));
+const WasmTripletLossFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_wasmtripletloss_free(ptr >>> 0, 1));
+
+function addHeapObject(obj) {
+    if (heap_next === heap.length) heap.push(heap.length + 1);
+    const idx = heap_next;
+    heap_next = heap[idx];
+
+    heap[idx] = obj;
+    return idx;
+}
+
+function _assertClass(instance, klass) {
+    if (!(instance instanceof klass)) {
+        throw new Error(`expected instance of ${klass.name}`);
+    }
+}
+
+function dropObject(idx) {
+    if (idx < 1028) return;
+    heap[idx] = heap_next;
+    heap_next = idx;
+}
+
+function getArrayF32FromWasm0(ptr, len) {
+    ptr = ptr >>> 0;
+    return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len);
+}
+
+let cachedDataViewMemory0 = null;
+function getDataViewMemory0() {
+    if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer.detached === true || (cachedDataViewMemory0.buffer.detached === undefined && cachedDataViewMemory0.buffer !== wasm.memory.buffer)) {
+        cachedDataViewMemory0 = new DataView(wasm.memory.buffer);
+    }
+    return cachedDataViewMemory0;
+}
+
+let cachedFloat32ArrayMemory0 = null;
+function getFloat32ArrayMemory0() {
+    if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.byteLength === 0) {
+        cachedFloat32ArrayMemory0 = new Float32Array(wasm.memory.buffer);
+    }
+    return cachedFloat32ArrayMemory0;
+}
+
+function getStringFromWasm0(ptr, len) {
+    ptr = ptr >>> 0;
+    return decodeText(ptr, len);
+}
+
+let cachedUint8ArrayMemory0 = null;
+function getUint8ArrayMemory0() {
+    if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.byteLength === 0) {
+        cachedUint8ArrayMemory0 = new Uint8Array(wasm.memory.buffer);
+    }
+    return cachedUint8ArrayMemory0;
+}
+
+function getObject(idx) { return heap[idx]; }
+
+let heap = new Array(1024).fill(undefined);
+heap.push(undefined, null, true, false);
+
+let heap_next = heap.length;
+
+function isLikeNone(x) {
+    return x === undefined || x === null;
+}
+
+function passArray8ToWasm0(arg, malloc) {
+    const ptr = malloc(arg.length * 1, 1) >>> 0;
+    getUint8ArrayMemory0().set(arg, ptr / 1);
+    WASM_VECTOR_LEN = arg.length;
+    return ptr;
+}
+
+function passArrayF32ToWasm0(arg, malloc) {
+    const ptr = malloc(arg.length * 4, 4) >>> 0;
+    getFloat32ArrayMemory0().set(arg, ptr / 4);
+    WASM_VECTOR_LEN = arg.length;
+    return ptr;
+}
+
+function passStringToWasm0(arg, malloc, realloc) {
+    if (realloc === undefined) {
+        const buf = cachedTextEncoder.encode(arg);
+        const ptr = malloc(buf.length, 1) >>> 0;
+        getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf);
+        WASM_VECTOR_LEN = buf.length;
+        return ptr;
+    }
+
+    let len = arg.length;
+    let ptr = malloc(len, 1) >>> 0;
+
+    const mem = getUint8ArrayMemory0();
+
+    let offset = 0;
+
+    for (; offset < len; offset++) {
+        const code = arg.charCodeAt(offset);
+        if (code > 0x7F) break;
+        mem[ptr + offset] = code;
+    }
+    if (offset !== len) {
+        if (offset !== 0) {
+            arg = arg.slice(offset);
+        }
+        ptr = realloc(ptr, len, len = offset + arg.length * 3, 1) >>> 0;
+        const view = getUint8ArrayMemory0().subarray(ptr + offset, ptr + len);
+        const ret = cachedTextEncoder.encodeInto(arg, view);
+
+        offset += ret.written;
+        ptr = realloc(ptr, len, offset, 1) >>> 0;
+    }
+
+    WASM_VECTOR_LEN = offset;
+    return ptr;
+}
+
+function takeObject(idx) {
+    const ret = getObject(idx);
+    dropObject(idx);
+    return ret;
+}
+
+let cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
+cachedTextDecoder.decode();
+const MAX_SAFARI_DECODE_BYTES = 2146435072;
+let numBytesDecoded = 0;
+function decodeText(ptr, len) {
+    numBytesDecoded += len;
+    if (numBytesDecoded >= MAX_SAFARI_DECODE_BYTES) {
+        cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
+        cachedTextDecoder.decode();
+        numBytesDecoded = len;
+    }
+    return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
+}
+
+const cachedTextEncoder = new TextEncoder();
+
+if (!('encodeInto' in cachedTextEncoder)) {
+    cachedTextEncoder.encodeInto = function (arg, view) {
+        const buf = cachedTextEncoder.encode(arg);
+        view.set(buf);
+        return {
+            read: arg.length,
+            written: buf.length
+        };
+    };
+}
+
+let WASM_VECTOR_LEN = 0;
+
+let wasmModule, wasm;
+function __wbg_finalize_init(instance, module) {
+    wasm = instance.exports;
+    wasmModule = module;
+    cachedDataViewMemory0 = null;
+    cachedFloat32ArrayMemory0 = null;
+    cachedUint8ArrayMemory0 = null;
+    wasm.__wbindgen_start();
+    return wasm;
+}
+
+async function __wbg_load(module, imports) {
+    if (typeof Response === 'function' && module instanceof Response) {
+        if (typeof WebAssembly.instantiateStreaming === 'function') {
+            try {
+                return await WebAssembly.instantiateStreaming(module, imports);
+            } catch (e) {
+                const validResponse = module.ok && expectedResponseType(module.type);
+
+                if (validResponse && module.headers.get('Content-Type') !== 'application/wasm') {
+                    console.warn("`WebAssembly.instantiateStreaming` failed because your server does not serve Wasm with `application/wasm` MIME type. Falling back to `WebAssembly.instantiate` which is slower. Original error:\n", e);
+
+                } else { throw e; }
+            }
+        }
+
+        const bytes = await module.arrayBuffer();
+        return await WebAssembly.instantiate(bytes, imports);
+    } else {
+        const instance = await WebAssembly.instantiate(module, imports);
+
+        if (instance instanceof WebAssembly.Instance) {
+            return { instance, module };
+        } else {
+            return instance;
+        }
+    }
+
+    function expectedResponseType(type) {
+        switch (type) {
+            case 'basic': case 'cors': case 'default': return true;
+        }
+        return false;
+    }
+}
+
+function initSync(module) {
+    if (wasm !== undefined) return wasm;
+
+
+    if (module !== undefined) {
+        if (Object.getPrototypeOf(module) === Object.prototype) {
+            ({module} = module)
+        } else {
+            console.warn('using deprecated parameters for `initSync()`; pass a single object instead')
+        }
+    }
+
+    const imports = __wbg_get_imports();
+    if (!(module instanceof WebAssembly.Module)) {
+        module = new WebAssembly.Module(module);
+    }
+    const instance = new WebAssembly.Instance(module, imports);
+    return __wbg_finalize_init(instance, module);
+}
+
+async function __wbg_init(module_or_path) {
+    if (wasm !== undefined) return wasm;
+
+
+    if (module_or_path !== undefined) {
+        if (Object.getPrototypeOf(module_or_path) === Object.prototype) {
+            ({module_or_path} = module_or_path)
+        } else {
+            console.warn('using deprecated parameters for the initialization function; pass a single object instead')
+        }
+    }
+
+    if (module_or_path === undefined) {
+        module_or_path = new URL('ruvector_cnn_wasm_bg.wasm', import.meta.url);
+    }
+    const imports = __wbg_get_imports();
+
+    if (typeof module_or_path === 'string' || (typeof Request === 'function' && module_or_path instanceof Request) || (typeof URL === 'function' && module_or_path instanceof URL)) {
+        module_or_path = fetch(module_or_path);
+    }
+
+    const { instance, module } = await __wbg_load(await module_or_path, imports);
+
+    return __wbg_finalize_init(instance, module);
+}
+
+export { initSync, __wbg_init as default };
Author	SHA1	Message	Date
ruv	0223ef6d2e	docs: add ADR-059 live ESP32 CSI pipeline + update README with demo links - ADR-059: Documents end-to-end ESP32 → sensing server → browser pipeline - README: Add dual-modal pose fusion demo link, update ADR count to 49 - References issue #245 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 17:40:16 -04:00
ruv	2f5e7ffb41	feat: live ESP32 CSI pipeline + auto-connect WebSocket - Add auto-connect to local sensing server WebSocket (ws://localhost:8765) - Demo shows "Live ESP32" when connected to real CSI data - Add build_firmware.ps1 for native Windows ESP-IDF builds (no Docker) - Add read_serial.ps1 for ESP32 serial monitor Pipeline: ESP32 → UDP:5005 → sensing-server → WS:8765 → browser demo Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 17:37:27 -04:00
ruv	4ce8ffc465	fix: video fills available space + correct WASM path resolution - Remove fixed aspect-ratio and max-height from video panel so it fills the available viewport space without scrolling - Grid uses 1fr row for content area, overflow:hidden on main grid - Fix WASM path: resolve relative to JS module file using import.meta.url instead of hardcoded ./pkg/ which resolved incorrectly on gh-pages - Responsive: mobile still gets aspect-ratio constraint Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 16:16:07 -04:00
ruv	3be63a7589	fix: motion-responsive skeleton + through-wall CSI tracking - Pose decoder now uses per-cell motion grid to track actual arm/head positions — raising arms moves the skeleton's arms, head follows lateral movement - Motion grid (10x8 cells) tracks intensity per body zone: head, left/right arm upper/mid, legs - Through-wall mode: when person exits frame, CSI maintains presence with slow decay (~10s) and skeleton drifts in exit direction - CSI simulator persists sensing after video loss, ghost pose renders with decreasing confidence - Reduced temporal smoothing (0.45) for faster response to movement Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 16:10:29 -04:00
ruv	c4e640c812	feat: dual-modal WASM browser pose estimation demo (ADR-058) Live webcam video + WiFi CSI fusion for real-time pose estimation. Two parallel CNN pipelines (ruvector-cnn-wasm) with attention-weighted fusion and dynamic confidence gating. Three modes: Dual, Video-only, CSI-only. Includes pre-built WASM package (~52KB) for browser deployment. - ADR-058: Dual-modal architecture design - ui/pose-fusion.html: Main demo page with dark theme UI - 7 JS modules: video-capture, csi-simulator, cnn-embedder, fusion-engine, pose-decoder, canvas-renderer, main orchestrator - Pre-built ruvector-cnn-wasm WASM package for browser - CSI heatmap, embedding space visualization, latency metrics - WebSocket support for live ESP32 CSI data - Navigation link added to main dashboard Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 14:26:39 -04:00
ruv	6e03a47867	docs: update user guide with v0.4.1 firmware release and CSI troubleshooting - Add v0.4.1 to firmware release table as recommended stable release - Update flash command with correct partition offsets (8MB, OTA) - Add "CSI not enabled" troubleshooting entry - Add warning about pre-v0.4.1 firmware CSI bug Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 13:49:20 -04:00
ruv	9d1140de2d	docs: update README firmware release table with v0.4.1 Add v0.4.1-esp32 as the recommended stable release and update the flash command to match the current partition layout. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 13:49:20 -04:00
ruv	952f27a1ce	fix(firmware): enable CSI in sdkconfig and add build guard (ADR-057) The committed sdkconfig had CONFIG_ESP_WIFI_CSI_ENABLED disabled, causing all builds to crash at runtime with "CSI not enabled in menuconfig". Root cause: sdkconfig.defaults.template existed but ESP-IDF only reads sdkconfig.defaults (no .template suffix). Fixes: - Add sdkconfig.defaults with CONFIG_ESP_WIFI_CSI_ENABLED=y - Add #error compile guard in csi_collector.c to prevent recurrence - Fix NVS encryption default (requires eFuse, breaks clean builds) Verified: Docker build + flash to ESP32-S3 + CSI callbacks confirmed. Closes #241 Relates to #223, #238, #234, #210, #190 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-12 13:49:20 -04:00
Reuven	f7d043d727	docs: fix Docker commands to use CSI_SOURCE environment variable The Docker image uses CSI_SOURCE env var to select the data source, not command-line arguments appended after the image name. Fixed: - ESP32 mode examples now use -e CSI_SOURCE=esp32 - Training mode example now uses --entrypoint override - Added CSI_SOURCE value table in Docker section Fixes #226 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-10 12:16:06 -04:00