feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion

mirror of https://github.com/ruvnet/RuView synced 2026-08-07 20:01:43 +00:00

* 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>

* 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>

* 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>

* 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>

* 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: RSSI visualization, RuVector attention WASM, cache-bust fixes

- Add animated RSSI Signal Strength panel with sparkline history
- Fix RuVector WasmMultiHeadAttention retptr calling convention
- Wire up RuVector Multi-Head + Flash Attention in CNN embedder
- Add ambient temporal drift to CSI simulator for visible heatmap animation
- Fix embedding space projection (sparse projection replaces cancelling sum)
- Add auto-scaling to embedding space renderer
- Add cache busters (?v=4) to all ES module imports to prevent stale caches
- Add diagnostic logging for module version verification
- Add RSSI tracking with quality labels and color-coded dBm display
- Includes ruvector-attention-wasm v2.0.5 browser ESM wrapper

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: 26-keypoint dexterous pose + full RuVector attention pipeline

Pose Decoder (17 → 26 keypoints):
- Add finger approximations: thumb, index, pinky per hand (6 new)
- Add toe tips: left/right foot index (2 new)
- Add neck keypoint (1 new)
- Hand openness driven by arm motion intensity
- Finger positions computed from wrist-elbow axis angles

CNN Embedder (full RuVector WASM pipeline):
- Stage 1: Multi-Head Attention (global spatial reasoning)
- Stage 2: Hyperbolic Attention (hierarchical body-part tree)
- Stage 3: MoE Attention (3 experts: upper/lower/extremities, top-2)
- Blended 40/30/30 weighting → final embedding projection

Canvas Renderer:
- Magenta finger joints with distinct glow
- Cyan toe tips
- White neck keypoint
- Thinner limb lines for hand/foot connections
- Joint count shown in overlay label

CSI Simulator:
- Skip synthetic person state when live ESP32 connected
- Only simulate CSI data in demo mode (was already correct)

Embedding Space:
- Fixed projection: sparse 8-dim projection replaces cancelling sum
- Auto-scaling normalizes point spread to fill canvas

Cache busters bumped to v=5 on all imports.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: centroid-based pose tracking for responsive limb movement

Rewrites pose decoder from intensity-based to position-based tracking:
- Arms now track toward motion centroid in each body zone
- Elbow/wrist positions computed along shoulder→centroid vector
- Legs track toward lower-body zone centroids
- Smoothing reduced from 0.45 to 0.25 for responsiveness
- Zone centroids blend 30% old / 70% new each frame

6 body zones with overlapping coverage:
- Head (top 20%, center cols)
- Left/Right Arm (rows 10-60%, outer cols)
- Torso (rows 15-55%, center cols)
- Left/Right Leg (rows 50-100%, half cols each)

Hand openness now driven by arm spread distance + raise amount.
Cache busters v=6.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: remove duplicate lAnkleX/rAnkleX declarations in pose-decoder

Stale code block from old intensity-based tracking was left behind,
re-declaring variables already defined by centroid-based tracking.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(demo): wire all 6 RuVector WASM attention mechanisms into pose fusion

- Add WasmLinearAttention and WasmLocalGlobalAttention to browser ESM wrapper
- Add 6 WASM utility functions (batch_normalize, pairwise_distances, etc.)
- Extend CnnEmbedder to 6-stage pipeline: Flash → MHA → Hyperbolic → Linear → MoE → L+G
- Use log-energy softmax blending across all 6 stages
- Wire WASM cosine_similarity and normalize into FusionEngine
- Add RuVector pipeline stats panel to UI (energy, refinement, pose impact)
- Compute embedding-to-joint mapping stats without modifying joint positions
- Center camera prompt with flexbox layout
- Add cache busters v=12

Co-Authored-By: claude-flow <ruv@ruv.net>

This commit is contained in:

rUv

2026-03-12 20:59:57 -04:00

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				@@ -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