my-cool-model

π RuView

<p align="center"> <a href="https://cognitum.one/seed"> <img src="assets/ruview-seed.png" alt="RuView - WiFi DensePose" width="100%"> </a> </p> <p align="center"> <a href="https://cognitum.one/seed"> <img src="assets/seed.png" alt="Cognitum Seed" width="100%"> </a> </p>

Beta Software — Under active development. APIs and firmware may change. Known limitations:

• ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
• Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a Cognitum Seed for best results
• Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — camera ground-truth training targets 35%+ PCK@20; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending, so no measured camera-supervised PCK@20 has been published yet

Contributions and bug reports welcome at Issues.

See through walls with WiFi

Turn ordinary WiFi into a spatial intelligence / sensing system. Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.

π RuView is a WiFi sensing platform that turns radio signals into spatial intelligence.

Every WiFi router already fills your space with radio waves. When people move, breathe, or even sit still, they disturb those waves in measurable ways. RuView captures these disturbances using Channel State Information (CSI) from low-cost ESP32 sensors and turns them into actionable data: who's there, what they're doing, and whether they're okay.

What it senses:

• Presence and occupancy — detect people through walls, count them, track entries and exits
• Vital signs — breathing rate and heart rate, contactless, while sleeping or sitting
• Activity recognition — walking, sitting, gestures, falls — from temporal CSI patterns
• Environment mapping — RF fingerprinting identifies rooms, detects moved furniture, spots new objects
• Sleep quality — overnight monitoring with sleep stage classification and apnea screening

Built on RuVector and Cognitum Seed, RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.

The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.

RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at ruvnet/wifi-densepose-pretrained — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized), runs in microseconds on a Raspberry Pi, and reports 100% presence accuracy on the validation set. No cameras, no wearables, no app on the user's phone.

Built for low-power edge applications

Edge modules are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.

What	How	Speed / scale
🫁 Breathing rate	Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM (#593)	6–30 BPM, real-time
💓 Heart rate	Bandpass 0.8–2.0 Hz, zero-crossing BPM	40–120 BPM, real-time
👤 Presence detection	Trained head on Hugging Face (ruvnet/wifi-densepose-pretrained, 100% validation accuracy) + a phase-variance fallback that needs no model	< 1 ms, ~30 s ambient calibration
🧬 CSI embeddings	128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB	164,183 emb/s on M4 Pro
🦴 17-keypoint pose estimation	cog-pose-estimation Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads pose_v1.safetensors via Candle. Train your own from paired data in 2.1 s on an RTX 5080 (ADR-101, benchmarks)	8.4 ms cold-start on a Pi 5
🚶 Motion / activity	Motion-band power + phase acceleration	Real-time
🤸 Fall detection	Phase-acceleration threshold + 3-frame debounce + 5 s cooldown (#263)	< 200 ms
🧮 Multi-person count	Adaptive P95 normalisation + runtime-tunable dedup factor (/api/v1/config/dedup-factor, #491). Six specialised learned counters available as Cogs: occupancy-zones, elevator-count, queue-length, customer-flow, clean-room, person-matching	Real-time, self-calibrating
🧱 Through-wall sensing	Fresnel-zone geometry + multipath modeling	Up to ~5 m, signal-dependent
🧠 Edge intelligence	105-cog catalog (ADR-102) live from app-registry.json — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain	$140 total BOM
🎯 Camera-free pre-training	Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face	84 s/epoch retrain on M4 Pro
📷 Camera-supervised fine-tune	MediaPipe + ESP32 CSI paired training, end-to-end Candle pipeline on RTX 5080 (ADR-079)	2.1 s for 400 epochs (~5 ms/epoch)
📡 Multi-frequency mesh	Channel hopping across 6 bands, TDM slot scheduling (ADR-029)	3× sensing bandwidth
🌐 3D point cloud fusion	Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model	22 ms pipeline · 19K+ points/frame

Browse the full 105-module catalog (with practical descriptions, sizes, and difficulty) below in 🧩 Edge Module Catalog, or visit seed.cognitum.one/store.

🤗 Pretrained weights: download from ruvnet/wifi-densepose-pretrained — see Loading the pretrained model below for one-command setup.

# Option 1: Docker (simulated data, no hardware needed)
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# Open http://localhost:3000

# Option 2: Live sensing with ESP32-S3 hardware ($9)
# Flash firmware, provision WiFi, and start sensing:
python -m esptool --chip esp32s3 --port COM9 --baud 460800 \
  write_flash 0x0 bootloader.bin 0x8000 partition-table.bin \
  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin
python firmware/esp32-csi-node/provision.py --port COM9 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20

# Option 3: Full system with Cognitum Seed ($140)
# ESP32 streams CSI → bridge forwards to Seed for persistent storage + kNN + witness chain
node scripts/rf-scan.js --port 5006           # Live RF room scan
node scripts/snn-csi-processor.js --port 5006  # SNN real-time learning
node scripts/mincut-person-counter.js --port 5006  # Correct person counting

[!NOTE] CSI-capable hardware recommended. Presence, vital signs, through-wall sensing, and all advanced capabilities require Channel State Information (CSI) from an ESP32-S3 ($9) or research NIC. The Docker image runs with simulated data for evaluation. Consumer WiFi laptops provide RSSI-only presence detection.

Hardware options for live CSI capture:

Option	Hardware	Cost	Full CSI	Capabilities
ESP32 + Cognitum Seed (recommended)	ESP32-S3 + Cognitum Seed	~$140	Yes	Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy
ESP32 Mesh	3-6x ESP32-S3 + WiFi router	~$54	Yes	Same capabilities as above without the persistent-memory features
Research NIC	Intel 5300 / Atheros AR9580	~$50-100	Yes	Full CSI with 3x3 MIMO
Any WiFi	Windows, macOS, or Linux laptop	$0	No	RSSI-only: coarse presence and motion (see tutorial #36)

No hardware? Verify the signal processing pipeline with the deterministic reference signal: python archive/v1/data/proof/verify.py

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<a href="https://ruvnet.github.io/RuView/"> <img src="assets/v2-screen.png" alt="WiFi DensePose — Live pose detection with setup guide" width="800"> </a> <br> <em>Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables</em> <br><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> &nbsp;|&nbsp; <a href="https://ruvnet.github.io/RuView/pointcloud/"><strong>▶ Live 3D Point Cloud</strong></a> &nbsp;|&nbsp; <a href="https://ruvnet.github.io/RuView/three.js/"><strong>▶ three.js Demos (5)</strong></a>

The server is optional for visualization and aggregation — the ESP32 runs independently for presence detection, vital signs, and fall alerts.

Live ESP32 pipeline: Connect an ESP32-S3 node → run the sensing server → open the pose fusion demo for real-time dual-modal pose estimation (webcam + WiFi CSI). See ADR-059.

three.js scene gallery at /three.js/ — five progressively richer ADR-097 demos: helpers, cinematic, GLTF skinned, FBX skinned, and a live MediaPipe→Mixamo retargeting feed driven by ESP32 CSI. Demos 04 and 05 require a local Mixamo X Bot.fbx (license boundary — not redistributed).

🤗 Pretrained model on Hugging Face

Pretrained CSI weights live at ruvnet/wifi-densepose-pretrained — 12.2M training steps on 60K frames / 610K contrastive triplets, 100% presence accuracy on the validation set, 4-bit quantized variant fits in 8 KB. The release includes a contrastive CSI encoder producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a presence-detection head. Per-node LoRA adapters are included for environment-specific fine-tuning.

# Download the model bundle
pip install huggingface_hub
huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wifi-densepose-pretrained

What works today vs. what's pending wiring:

Known gap: the HF model ships in JSONL RVF format, but v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs only parses the binary RVF segment format. Pointing --model at model.rvf.jsonl currently errors with invalid magic at offset 0: expected 0x52564653, got 0x7974227B and the live pipeline degrades to null output rather than falling back to heuristic mode — so for the live sensing-server, run without --model until a JSONL adapter lands (or the model is re-published as binary RVF). Use the weights from Python / training in the meantime.

Quantization choices (all in the HF repo): model-q2.bin (4 KB) · model-q4.bin ⭐ recommended (8 KB) · model-q8.bin (16 KB) · model.safetensors full (48 KB)

The separate 17-keypoint pose-estimation model is not in this release — pipeline is implemented but keypoint weights are still pending. Tracked in #509; see ADR-079 phases P7–P9.

🧩 Edge Module Catalog

<details> <summary><b>🧩 105 edge modules ready to install on a Cognitum appliance</b> &mdash; live catalog from <code>app-registry.json</code> v2.1.0 (updated 2026-05-13). Browse + install at <a href="https://seed.cognitum.one/store">seed.cognitum.one/store</a> or your local appliance <code>http://&lt;appliance&gt;:9000/cogs</code>.</summary>

Each module is a small signed binary (~400 KB) that runs alongside the WiFi-DensePose sensing stack on a Cognitum-V0 appliance. The catalog updates over the air — your appliance fetches it via <code>GET /api/v1/edge/registry</code> (ADR-102) and verifies each binary against an Ed25519 signature (ADR-100) before install.

🫀 Health — _{14 modules}

🔒 Security — _{14 modules}

🏢 Building — _{11 modules}

🛍️ Retail — _{7 modules}

🏭 Industrial — _{7 modules}

🔬 Research — _{12 modules}

🤖 Ai — _{15 modules}

🐝 Swarm — _{11 modules}

📡 Signal — _{6 modules}

🌐 Network — _{1 modules}

🛠️ Developer — _{7 modules}

ℹ️ Build your own cog: see ADR-100 for the packaging spec. The first cog this repo ships into the catalog lives in v2/crates/cog-pose-estimation/ (17-keypoint WiFi pose, ADR-101).

</details>

🔬 How It Works

WiFi routers flood every room with radio waves. When a person moves — or even breathes — those waves scatter differently. WiFi DensePose reads that scattering pattern and reconstructs what happened:

WiFi Router → radio waves pass through room → hit human body → scatter
    ↓
ESP32 mesh (4-6 nodes) captures CSI on channels 1/6/11 via TDM protocol
    ↓
Multi-Band Fusion: 3 channels × 56 subcarriers = 168 virtual subcarriers per link
    ↓
Multistatic Fusion: N×(N-1) links → attention-weighted cross-viewpoint embedding
    ↓
Coherence Gate: accept/reject measurements → stable for days without tuning
    ↓
Signal Processing: Hampel, SpotFi, Fresnel, BVP, spectrogram → clean features
    ↓
AI Backbone (RuVector): attention, graph algorithms, compression, field model
    ↓
Signal-Line Protocol (CRV): 6-stage gestalt → sensory → topology → coherence → search → model
    ↓
Neural Network: processed signals → 17 body keypoints + vital signs + room model
    ↓
Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts

No training cameras required — the Self-Learning system (ADR-024) bootstraps from raw WiFi data alone. MERIDIAN (ADR-027) ensures the model works in any room, not just the one it trained in.

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🏢 Use Cases & Applications

WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just software on existing access points or a $8 ESP32 add-on. Because there are no cameras, deployments avoid privacy regulations (GDPR video, HIPAA imaging) by design.

Scaling: Each AP distinguishes ~3-5 people (56 subcarriers). Multi-AP multiplies linearly — a 4-AP retail mesh covers ~15-20 occupants. No hard software limit; the practical ceiling is signal physics.

<details> <summary><strong>🏥 Everyday</strong> — Healthcare, retail, office, hospitality (commodity WiFi)</summary> </details> <details> <summary><strong>🏟️ Specialized</strong> — Events, fitness, education, civic (CSI-capable hardware)</summary> </details> <details> <summary><strong>🤖 Robotics & Industrial</strong> — Autonomous systems, manufacturing, android spatial awareness</summary>

WiFi sensing gives robots and autonomous systems a spatial awareness layer that works where LIDAR and cameras fail — through dust, smoke, fog, and around corners. The CSI signal field acts as a "sixth sense" for detecting humans in the environment without requiring line-of-sight.

</details> <details> <summary><strong>🔥 Extreme</strong> — Through-wall, disaster, defense, underground</summary>

These scenarios exploit WiFi's ability to penetrate solid materials — concrete, rubble, earth — where no optical or infrared sensor can reach. The WiFi-Mat disaster module (ADR-001) is specifically designed for this tier.

</details>

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<details> <summary><strong>🧠 Self-Learning WiFi AI (ADR-024)</strong> — Adaptive recognition, self-optimization, and intelligent anomaly detection</summary>

Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment.

What it does in plain terms:

• Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
• Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
• Recognizes rooms, detects intruders, identifies people, and classifies activities using only WiFi
• Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory)
• Produces both body pose tracking AND environment fingerprints in a single computation

Key Capabilities

Architecture

WiFi Signal [56 channels] → Transformer + Graph Neural Network
                                  ├→ 128-dim environment fingerprint (for search + identification)
                                  └→ 17-joint body pose (for human tracking)

Quick Start

# Step 1: Learn from raw WiFi data (no labels needed)
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50

# Step 2: Fine-tune with pose labels for full capability
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf

# Step 3: Use the model — extract fingerprints from live WiFi
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed

# Step 4: Search — find similar environments or detect anomalies
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env

Training Modes

Fingerprint Index Types

Model Size

The self-learning system builds on the AI Backbone (RuVector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top.

See docs/adr/ADR-024-contrastive-csi-embedding-model.md for full architectural details.

</details>

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🧩 Claude Code & Codex Plugin

RuView ships a Claude Code plugin (and Codex prompt mirror) that wraps the whole workflow — onboarding, ESP32 setup, configuration, sensing apps, model training, advanced multistatic sensing, CLI/API/WASM, mmWave radar, and witness verification — as 9 skills, 7 /ruview-* commands, and 3 agents. It lives in plugins/ruview/; the marketplace manifest is .claude-plugin/marketplace.json at the repo root.

# In Claude Code — add this repo as a plugin marketplace, then install:
/plugin marketplace add ruvnet/RuView
/plugin install ruview@ruview

# Or try it for one session without installing (from a local clone of the repo):
claude --plugin-dir ./plugins/ruview

# Then, in Claude Code:
#   /ruview-start      → onboarding (Docker demo / repo build / live ESP32)
#   /ruview-flash      → build + flash ESP32 firmware
#   /ruview-provision  → provision WiFi creds, sink IP, channel/MAC, mesh slots
#   /ruview-app        → run a sensing application (presence / vitals / pose / sleep / MAT / point cloud)
#   /ruview-train      → train / evaluate / publish a model (incl. GPU on GCloud)
#   /ruview-advanced   → multistatic / tomography / cross-viewpoint / mesh-security
#   /ruview-verify     → tests + deterministic proof + witness bundle

Codex (OpenAI CLI): cp plugins/ruview/codex/prompts/*.md ~/.codex/prompts/ — the seven /ruview-* commands are mirrored as Codex prompts; plugins/ruview/codex/AGENTS.md carries the project rules. See plugins/ruview/codex/README.md.

Verify the plugin structure: bash plugins/ruview/scripts/smoke.sh. Full details: plugins/ruview/README.md.

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📖 Documentation

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📄 License

MIT License — see LICENSE for details.

📞 Support

GitHub Issues | Discussions | PyPI

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WiFi DensePose — Privacy-preserving human pose estimation through WiFi signals.