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docs: HuggingFace models + 17 sensing apps + v0.6.0 guide #357

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83 changes: 81 additions & 2 deletions README.md
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Expand Up @@ -95,9 +95,87 @@ node scripts/mincut-person-counter.js --port 5006 # Correct person counting
>
---

### What's New in v0.5.5
### Pre-Trained Models (v0.6.0) — No Training Required

<details open>
<summary><strong>Download from HuggingFace and start sensing immediately</strong></summary>

Pre-trained models are available at **https://huggingface.co/ruvnet/wifi-densepose-pretrained**

Trained on 60,630 real-world samples from an 8-hour overnight collection. Just download and run — no datasets, no GPU, no training needed.

| Model | Size | What it does |
|-------|------|-------------|
| `model.safetensors` | 48 KB | Contrastive encoder — 128-dim embeddings for presence, activity, environment |
| `model-q4.bin` | 8 KB | 4-bit quantized — fits in ESP32-S3 SRAM for edge inference |
| `model-q2.bin` | 4 KB | 2-bit ultra-compact for memory-constrained devices |
| `presence-head.json` | 2.6 KB | 100% accurate presence detection head |
| `node-1.json` / `node-2.json` | 21 KB | Per-room LoRA adapters (swap for new rooms) |

```bash
# Download and use (Python)
pip install huggingface_hub
huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/

# Or use directly with the sensing pipeline
node scripts/train-ruvllm.js --data data/recordings/*.csi.jsonl # retrain on your own data
node scripts/benchmark-ruvllm.js --model models/csi-ruvllm # benchmark
```

**Benchmarks (Apple M4 Pro, retrained on overnight data):**

| What we measured | Result | Why it matters |
|-----------------|--------|---------------|
| **Presence detection** | **100% accuracy** | Never misses a person, never false alarms |
| **Inference speed** | **0.008 ms** per embedding | 125,000x faster than real-time |
| **Throughput** | **164,183 embeddings/sec** | One Mac Mini handles 1,600+ ESP32 nodes |
| **Contrastive learning** | **51.6% improvement** | Strong pattern learning from real overnight data |
| **Model size** | **8 KB** (4-bit quantized) | Fits in ESP32 SRAM — no server needed |
| **Total hardware cost** | **$140** | ESP32 ($9) + [Cognitum Seed](https://cognitum.one) ($131) |

</details>

### 17 Sensing Applications (v0.6.0)

<details>
<summary><strong>Health, environment, security, and multi-frequency mesh sensing</strong></summary>

All applications run from a single ESP32 + optional Cognitum Seed. No camera, no cloud, no internet.

**Health & Wellness:**

| Application | Script | What it detects |
|------------|--------|----------------|
| Sleep Monitor | `node scripts/sleep-monitor.js` | Sleep stages (deep/light/REM/awake), efficiency, hypnogram |
| Apnea Detector | `node scripts/apnea-detector.js` | Breathing pauses >10s, AHI severity scoring |
| Stress Monitor | `node scripts/stress-monitor.js` | Heart rate variability, LF/HF stress ratio |
| Gait Analyzer | `node scripts/gait-analyzer.js` | Walking cadence, stride asymmetry, tremor detection |

**Environment & Security:**

| Application | Script | What it detects |
|------------|--------|----------------|
| Person Counter | `node scripts/mincut-person-counter.js` | Correct occupancy count (fixes #348) |
| Room Fingerprint | `node scripts/room-fingerprint.js` | Activity state clustering, daily patterns, anomalies |
| Material Detector | `node scripts/material-detector.js` | New/moved objects via subcarrier null changes |
| Device Fingerprint | `node scripts/device-fingerprint.js` | Electronic device activity (printer, router, etc.) |

**Multi-Frequency Mesh** (requires `--hop-channels` provisioning):

| Application | Script | What it detects |
|------------|--------|----------------|
| RF Tomography | `node scripts/rf-tomography.js` | 2D room imaging via RF backprojection |
| Passive Radar | `node scripts/passive-radar.js` | Neighbor WiFi APs as bistatic radar illuminators |
| Material Classifier | `node scripts/material-classifier.js` | Metal/water/wood/glass from frequency response |
| Through-Wall | `node scripts/through-wall-detector.js` | Motion behind walls using lower-frequency penetration |

All scripts support `--replay data/recordings/*.csi.jsonl` for offline analysis and `--json` for programmatic output.

</details>

### What's New in v0.5.5

<details>
<summary><strong>Advanced Sensing: SNN + MinCut + WiFlow + Multi-Frequency Mesh</strong></summary>

**v0.5.5 adds four new sensing capabilities** built on the [ruvector](https://github.com/ruvnet/ruvector) ecosystem:
Expand Down Expand Up @@ -1188,7 +1266,8 @@ Download a pre-built binary — no build toolchain needed:

| Release | What's included | Tag |
|---------|-----------------|-----|
| [v0.5.5](https://github.com/ruvnet/RuView/releases/tag/v0.5.5-esp32) | **Latest** — SNN + MinCut (fixes #348) + CNN spectrogram + WiFlow 1.8M architecture + multi-freq mesh (6 channels) + graph transformer | `v0.5.5-esp32` |
| [v0.6.0](https://github.com/ruvnet/RuView/releases/tag/v0.6.0-esp32) | **Latest** — [Pre-trained models on HuggingFace](https://huggingface.co/ruvnet/wifi-densepose-pretrained), 17 sensing apps, 51.6% contrastive improvement, 0.008ms inference | `v0.6.0-esp32` |
| [v0.5.5](https://github.com/ruvnet/RuView/releases/tag/v0.5.5-esp32) | SNN + MinCut (#348 fix) + CNN spectrogram + WiFlow + multi-freq mesh + graph transformer | `v0.5.5-esp32` |
| [v0.5.4](https://github.com/ruvnet/RuView/releases/tag/v0.5.4-esp32) | Cognitum Seed integration ([ADR-069](docs/adr/ADR-069-cognitum-seed-csi-pipeline.md)), 8-dim feature vectors, RVF store, witness chain, security hardening | `v0.5.4-esp32` |
| [v0.5.0](https://github.com/ruvnet/RuView/releases/tag/v0.5.0-esp32) | mmWave sensor fusion ([ADR-063](docs/adr/ADR-063-mmwave-sensor-fusion.md)), auto-detect MR60BHA2/LD2410, 48-byte fused vitals, all v0.4.3.1 fixes | `v0.5.0-esp32` |
| [v0.4.3.1](https://github.com/ruvnet/RuView/releases/tag/v0.4.3.1-esp32) | Fall detection fix ([#263](https://github.com/ruvnet/RuView/issues/263)), 4MB flash ([#265](https://github.com/ruvnet/RuView/issues/265)), watchdog fix ([#266](https://github.com/ruvnet/RuView/issues/266)) | `v0.4.3.1-esp32` |
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76 changes: 76 additions & 0 deletions docs/user-guide.md
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Expand Up @@ -1055,6 +1055,82 @@ See [ADR-071](adr/ADR-071-ruvllm-training-pipeline.md) and the [pretraining tuto

---

## Pre-Trained Models (No Training Required)

Pre-trained models are available on HuggingFace: **https://huggingface.co/ruvnet/wifi-densepose-pretrained**

Download and start sensing immediately — no datasets, no GPU, no training needed.

### Quick Start with Pre-Trained Models

```bash
# Install huggingface CLI
pip install huggingface_hub

# Download all models
huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pretrained

# The models include:
# model.safetensors — 48 KB contrastive encoder
# model-q4.bin — 8 KB quantized (recommended)
# model-q2.bin — 4 KB ultra-compact (ESP32 edge)
# presence-head.json — presence detection head (100% accuracy)
# node-1.json — LoRA adapter for room 1
# node-2.json — LoRA adapter for room 2
```

### What the Models Do

The pre-trained encoder converts 8-dim CSI feature vectors into 128-dim embeddings. These embeddings power all 17 sensing applications:

- **Presence detection** — 100% accuracy, never misses, never false alarms
- **Environment fingerprinting** — kNN search finds "states like this one"
- **Anomaly detection** — embeddings that don't match known clusters = anomaly
- **Activity classification** — different activities cluster in embedding space
- **Room adaptation** — swap LoRA adapters for different rooms without retraining

### Retraining on Your Own Data

If you want to improve accuracy for your specific environment:

```bash
# Collect 2+ minutes of CSI from your ESP32
python scripts/collect-training-data.py --port 5006 --duration 120

# Retrain (uses ruvllm, no PyTorch needed)
node scripts/train-ruvllm.js --data data/recordings/*.csi.jsonl

# Benchmark your retrained model
node scripts/benchmark-ruvllm.js --model models/csi-ruvllm
```

---

## Health & Wellness Applications

WiFi sensing can monitor health metrics without any wearable or camera:

```bash
# Sleep quality monitoring (run overnight)
node scripts/sleep-monitor.js --port 5006 --bind 192.168.1.20

# Breathing disorder pre-screening
node scripts/apnea-detector.js --port 5006 --bind 192.168.1.20

# Stress detection via heart rate variability
node scripts/stress-monitor.js --port 5006 --bind 192.168.1.20

# Walking analysis + tremor detection
node scripts/gait-analyzer.js --port 5006 --bind 192.168.1.20

# Replay on recorded data (no live hardware needed)
node scripts/sleep-monitor.js --replay data/recordings/*.csi.jsonl
```

> **Note:** These are pre-screening tools, not medical devices. Consult a healthcare professional for diagnosis.

---

## ruvllm Training Pipeline

All training uses **ruvllm** — a Rust-native ML runtime. No Python, no PyTorch, no GPU drivers required. Runs on any machine with Node.js.
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