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docs: update README + user guide with v0.5.4 capabilities

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README:
- Test badge 1300+ → 1463
- Updated capability table (171K emb/s, 100% presence, 0.012ms)
- Added "What's New in v0.5.4" section with full benchmark table
- Training pipeline quick start commands

User guide:
- Camera-Free Pose Training section (10 sensor signals, 5-phase pipeline)
- ruvllm Training Pipeline section (5 phases, quantization options)
- Publishing to HuggingFace section
- Updated table of contents

Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-04-02 23:11:30 -04:00
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README.md
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@@ -40,7 +40,7 @@ In practice this means ordinary environments gain a new kind of spatial awarenes
[![Rust 1.85+](https://img.shields.io/badge/rust-1.85+-orange.svg)](https://www.rust-lang.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Tests: 1300+](https://img.shields.io/badge/tests-1300%2B-brightgreen.svg)](https://github.com/ruvnet/RuView)
[![Tests: 1463](https://img.shields.io/badge/tests-1463%20passed-brightgreen.svg)](https://github.com/ruvnet/RuView)
[![Docker: multi-arch](https://img.shields.io/badge/docker-amd64%20%2B%20arm64-blue.svg)](https://hub.docker.com/r/ruvnet/wifi-densepose)
[![Vital Signs](https://img.shields.io/badge/vital%20signs-breathing%20%2B%20heartbeat-red.svg)](#vital-sign-detection)
[![ESP32 Ready](https://img.shields.io/badge/ESP32--S3-CSI%20streaming-purple.svg)](#esp32-s3-hardware-pipeline)
@@ -49,11 +49,13 @@ In practice this means ordinary environments gain a new kind of spatial awarenes
> | What | How | Speed |
> |------|-----|-------|
> | **Pose estimation** | CSI subcarrier amplitude/phase → DensePose UV maps | 54K fps (Rust) |
> | **Breathing detection** | Bandpass 0.1-0.5 Hz → FFT peak | 6-30 BPM |
> | **Heart rate** | Bandpass 0.8-2.0 Hz → FFT peak | 40-120 BPM |
> | **Presence sensing** | RSSI variance + motion band power | < 1ms latency |
> | **Pose estimation** | CSI subcarrier amplitude/phase → 17 COCO keypoints | 171K emb/s (M4 Pro) |
> | **Breathing detection** | Bandpass 0.1-0.5 Hz → zero-crossing BPM | 6-30 BPM |
> | **Heart rate** | Bandpass 0.8-2.0 Hz → zero-crossing BPM | 40-120 BPM |
> | **Presence sensing** | Trained model + PIR fusion — 100% accuracy | 0.012 ms latency |
> | **Through-wall** | Fresnel zone geometry + multipath modeling | Up to 5m depth |
> | **Edge intelligence** | 8-dim feature vectors + RVF store on Cognitum Seed | $27 total BOM |
> | **Camera-free training** | 10 sensor signals, no labels needed | 84s on M4 Pro |
```bash
# 30 seconds to live sensing — no toolchain required
@@ -78,6 +80,61 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
>
---
### What's New in v0.5.4
<details open>
<summary><strong>Cognitum Seed Integration + Camera-Free Pose Training</strong></summary>
**v0.5.4 transforms RuView from a real-time sensing tool into a persistent edge AI system.** Your ESP32 now remembers what it senses, learns without cameras, and proves its data cryptographically.
| Capability | Details | Hardware |
|-----------|---------|----------|
| **Persistent vector store** | Every sensing event stored as searchable 8-dim vector in RVF format | ESP32 + [Cognitum Seed](https://cognitum.one) ($27) |
| **kNN similarity search** | "Find the 10 most similar states to right now" — anomaly detection, fingerprinting | Cognitum Seed |
| **Witness chain** | SHA-256 tamper-evident audit trail for every measurement (1,747 entries validated) | Cognitum Seed |
| **Camera-free pose training** | 17 COCO keypoints from 10 sensor signals — PIR, RSSI triangulation, subcarrier asymmetry, vibration, BME280 | 2x ESP32 + Seed |
| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 100% presence accuracy, 0 skeleton violations | Download from release |
| **Sub-ms inference** | 0.012 ms latency, 171,472 embeddings/sec on M4 Pro | Any machine with Node.js |
| **SONA adaptation** | Adapts to new rooms in <1ms without retraining | ruvllm runtime |
| **LoRA room adapters** | Per-node fine-tuning with 2,048 parameters per adapter | Automatic |
| **114-tool MCP proxy** | AI assistants (Claude, GPT) query sensors directly via JSON-RPC | Cognitum Seed |
| **Security hardened** | Bearer tokens, TLS, source IP filtering, NaN rejection, credential rotation | All components |
**Training pipeline (ruvllm, no PyTorch needed):**
```bash
# Collect data (2 min, ESP32s must be streaming)
python scripts/collect-training-data.py --port 5006 --duration 120
# Train — contrastive pretraining + task heads + LoRA + quantization + EWC
node scripts/train-ruvllm.js --data data/recordings/pretrain-*.csi.jsonl
# Camera-free 17-keypoint pose (uses PIR + RSSI + vibration + subcarrier asymmetry)
node scripts/train-camera-free.js --data data/recordings/pretrain-*.csi.jsonl
# Benchmark
node scripts/benchmark-ruvllm.js --model models/csi-ruvllm
```
**Validated benchmarks (M4 Pro):**
| Metric | Value |
|--------|-------|
| Training time | 84.4s (2,360 augmented samples) |
| Contrastive improvement | 33.9% |
| Presence accuracy | 100% |
| Inference latency | 0.012 ms |
| Throughput | 171,472 emb/s |
| Model size (4-bit) | 8 KB |
| Skeleton violations | 0 / 100 frames |
| Rust tests | 1,463 passed |
See [ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md), [ADR-071](docs/adr/ADR-071-ruvllm-training-pipeline.md), and the [Cognitum Seed tutorial](docs/tutorials/cognitum-seed-pretraining.md) for full details.
</details>
---
## 📖 Documentation
| Document | Description |