ruvnet/RuView｜GitHub 仓库详情

ruvnet/RuView｜GitHub 仓库详情 | 极客日志

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; v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + 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). SOTA on MM-Fi: ruvnet/wifi-densepose-mmfi-pose hits 82.69% torso-PCK@20 (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi random_split protocol — self-corrected and auditable on AetherArena 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
🌍 World model prediction OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with occworld_retrain.py (ADR-147) 15 frames × 200×200×16 vox
🧱 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.

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`; v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + 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). SOTA on MM-Fi: `ruvnet/wifi-densepose-mmfi-pose` hits 82.69% torso-PCK@20 (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on AetherArena	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
🌍 World model prediction	OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with `occworld_retrain.py` (ADR-147)	15 frames × 200×200×16 vox
🧱 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

# 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 2a: 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 2b: WiFi 6 + 802.15.4 research sensing with ESP32-C6 ($6-10, ADR-110)
# Same csi-node firmware compiled for the C6 target — picks up the C6
# overlay (sdkconfig.defaults.esp32c6) automatically.
cd firmware/esp32-csi-node
idf.py set-target esp32c6 && idf.py build
idf.py -p COM6 flash
# C6 boot extras (vs S3): HE-LTF subcarrier tagging in ADR-018 bytes 18-19,
#   802.15.4 mesh time-sync on channel 15, TWT setup when the AP supports it,
#   opt-in LP-core wake-on-motion for ~5 µA battery seed nodes.
# v0.6.7 adds: real LP-core RISC-V motion-gate program (debounce + motion
#   counter) and a Wi-Fi 6 soft-AP with TWT Responder so two C6 boards can
#   benchmark real iTWT without buying an 11ax router. Both default off,
#   flip CONFIG_C6_{LP_CORE,SOFTAP_HE}_ENABLE to turn them on.

# 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

# Option 4: Python — live on PyPI (ADR-117)
pip install ruview                        # or: pip install wifi-densepose
# Both ship the same compiled PyO3 wheel (~250 KB, abi3-py310, Linux/macOS/Windows).
# Add [client] for the asyncio WebSocket + paho-mqtt clients:
pip install "ruview[client]"              # or: pip install "wifi-densepose[client]"

# from ruview import BreathingExtractor, HeartRateExtractor   # equivalent to:
# from wifi_densepose import BreathingExtractor, HeartRateExtractor
# from ruview.client import SensingClient, RuViewMqttClient

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 3-6× ESP32-S3 + WiFi router ~$54 Yes Same capabilities as above without the persistent-memory features
ESP32-C6 research node (ADR-110, witness, reviewer guide, firmware v0.7.0) ESP32-C6-DevKit ($6–10) ~$10 Yes (Wi-Fi 6 capable) Same CSI pipeline as S3 with the dual-target firmware. Firmware-side ADR-110 substrate now closed (v0.7.0): ESP-NOW cross-board mesh quantified at 99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). Hardware-gated for measurement: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split.
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

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	3-6× ESP32-S3 + WiFi router	~$54	Yes	Same capabilities as above without the persistent-memory features
ESP32-C6 research node (ADR-110, witness, reviewer guide, firmware v0.7.0)	ESP32-C6-DevKit ($6–10)	~$10	Yes (Wi-Fi 6 capable)	Same CSI pipeline as S3 with the dual-target firmware. Firmware-side ADR-110 substrate now closed (v0.7.0): ESP-NOW cross-board mesh quantified at 99.56 % match / 104 µs smoothed offset stdev / 3.95× EMA suppression over a 5-min two-board soak (witness §A0.10), 32-byte UDP sync packet with operator-tunable cadence (§A0.12), ADR-018 byte 19 bit 4 wire-fix sourced from the working ESP-NOW path (§A0.13). Wire format ready for HE-LTF PPDU tagging in ADR-018 bytes 18-19 (firmware encoder + Rust + Python decoders verified end-to-end across 23 unit tests). LP-core motion-gate RISC-V program and Wi-Fi 6 soft-AP with TWT Responder both ship as opt-in code paths (default off). Hardware-gated for measurement: HE-LTF live subcarrier capture needs an 11ax AP (IDF v5.4 doesn't expose AP-side HE config — §A0.6); ~5 µA LP-core hibernation needs an INA meter to capture; 802.15.4 raw RX is broken in IDF v5.4 (workaround: ESP-NOW transport, shipped + measured). See witness log for the empirical / claimed split.
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)

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

Consumer	Format used	Status
Python training / evaluation / embedding extraction	`model.safetensors`	✅ Works — load with `safetensors.torch.load_file`
Inspect / re-export the bundle	`model.rvf.jsonl` (line-by-line JSON)	✅ Works — plain JSONL
Sensing-server `--model <PATH>` flag	binary RVF (`RVFS` magic)	⚠️ Loader does not yet accept the JSONL container

What	Where	Numbers
MM-Fi pose model (SOTA)	`ruvnet/wifi-densepose-mmfi-pose`	82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30%
AetherArena benchmark Space	`ruvnet/aether-arena`	self-correcting, auditable MM-Fi leaderboard
Full MM-Fi study (honest picture)	`docs/benchmarks/mmfi-wifi-sensing-study.md`	pose + action; zero-shot cross-subject ~64%, +~30 s in-room calibration → 72.2%
Efficiency frontier	`docs/benchmarks/wifi-pose-efficiency-frontier.md`	SOTA-beating WiFi pose in a 20 KB int4 edge model
Pretrained encoder	`ruvnet/wifi-densepose-pretrained`	82.3% held-out temporal-triplet, 8 KB int4
Reproducible proof (Trust Kill Switch)	`archive/v1/data/proof/verify.py` + `expected_features.sha256`	one-command deterministic pipeline replay (SHA-256 of output vs published hash)
Benchmark-proof ADR	ADR-168	how the numbers are produced and verified
Witness attestation	`docs/WITNESS-LOG-028.md`	33-row capability attestation matrix with per-claim evidence

# Reproduce the deterministic pipeline proof yourself (must print VERDICT: PASS):
python archive/v1/data/proof/verify.py

🧩 105 edge modules ready to install on a Cognitum appliance — live catalog from app-registry.json v2.1.0 (updated 2026-05-13). Browse + install at seed.cognitum.one/store or your local appliance http://<appliance>:9000/cogs.

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 GET /api/v1/edge/registry (ADR-102) and verifies each binary against an Ed25519 signature (ADR-100) before install.

🫀 Health — _{14 modules}

ID	What it does	Size	Difficulty
`air-quality-index`	Track indoor air quality with CO2 and particle sensors	8 KB	Easy
`baby-cry`	Sustained mid-band energy detector for nursery / infant monitoring. Audio-only, no camera.	451 KB	Easy
`breathing-sync`	Detects when two people breathe in sync	10 KB	Hard
`cardiac-arrhythmia`	Spots irregular heartbeats and abnormal heart rhythms	8 KB	Hard
`cough-detect`	Acoustic transient + spectral cough detector with 30s cluster aggregation. Early-warning signal for respiratory illness.	451 KB	Easy
`dream-stage`	Tracks your sleep stages — light, deep, and dreaming	14 KB	Hard
`fall-detect`	Two-stage impact + stillness fall detector over ambient feature stream (ESP32 motion / mic). Optional ruview-mode for CSI-based pose reinforcement.	402 KB	Easy
`gait-analysis`	Detects walking problems and scores fall risk	12 KB	Hard
`health-monitor`	Contactless heart rate, breathing, sleep, and fall alerts	30 KB	Med
`respiratory-distress`	Alerts when breathing becomes labored or dangerously fast	10 KB	Hard
`seizure-detect`	Recognizes seizures and sends immediate alerts	10 KB	Hard
`sleep-apnea`	Detects when someone stops breathing during sleep	4 KB	Easy
`snore-monitor`	Periodic low-band energy tracker for sleep-quality / apnea-risk trending. Companion to sleep-apnea cog.	451 KB	Easy
`vital-trend`	Tracks breathing and heart rate trends over weeks	6 KB	Med

🔒 Security — _{14 modules}

ID	What it does	Size	Difficulty
`audit-logger`	Record every action for compliance — tamper-proof log	8 KB	Easy
`behavioral-profiler`	Learns normal behavior and flags anything unusual	12 KB	Hard
`fleet-auth`	Manage device certificates and access across all seeds	12 KB	Med
`glass-break`	Two-phase bang + shatter acoustic detector. Distinguishes glass break from ordinary impulse noise.	451 KB	Easy
`gunshot-detect`	Saturating peak + exponential decay acoustic detector with optional ruview CSI motion-drop reinforcement.	451 KB	Easy
`intrusion`	Alerts when an unauthorized person enters a room	6 KB	Med
`intrusion-detect-ml`	Detect network attacks using machine learning	14 KB	Hard
`loitering`	Alerts when someone lingers too long in one spot	3 KB	Easy
`network-firewall`	Block unauthorized network access per cog	6 KB	Easy
`panic-motion`	Detects sudden panicked or erratic movement	6 KB	Med
`perimeter-breach`	Guards multiple zones and shows entry direction	10 KB	Med
`prompt-shield`	Blocks signal replay and injection attacks on the seed	10 KB	Med
`tailgating`	Catches when someone sneaks in behind a badge holder	6 KB

🏢 Building — _{11 modules}

ID	What it does	Size	Difficulty
`beehive-monitor`	Acoustic hive state classifier. Detects healthy / chaotic / queenless / swarming / robbing via hum-band energy + chaos + piping autocorr.	451 KB	Easy
`elevator-count`	Counts how many people are in an elevator	8 KB	Med
`energy-audit`	Learns your schedule and cuts wasted energy	6 KB	Med
`frost-warning`	Predicts frost 6 hours ahead via temperature trend + dewpoint-depression gate. Field/orchard agriculture.	451 KB	Easy
`hvac-presence`	Turns heating and cooling on when you arrive	3 KB	Easy
`lighting-zones`	Turns lights on and off as people move between rooms	4 KB	Easy
`meeting-room`	Shows if a meeting room is free or occupied	5 KB	Easy
`occupancy-zones`	Counts people in each room through walls	8 KB	Med
`predictive-maintenance`	Vibration harmonic analyzer for rotating equipment. Tracks F1 / 2×F1 / high-order / sideband energy to score degradation severity.	451 KB	Easy
`smoke-fire`	Multi-signal smoke and fire detector. Fuses acoustic crackle, thermal drift proxy, and optional ruview CSI plume signature. Not a UL-listed replacement for code-required smoke alarms.	451 KB	Easy
`water-leak`	Persistent low-amplitude hiss + periodic drip acoustic detector with multi-minute persistence gate. Two-stage likely → confirmed.	451 KB	Easy

🛍️ Retail — _{7 modules}

ID	What it does	Size	Difficulty
`customer-flow`	Counts foot traffic in and out of each entrance	8 KB	Med
`dwell-heatmap`	Shows where customers spend the most time	6 KB	Med
`package-detect`	Sustained CSI-shift detector for porch / loading bay package arrivals and departures. Requires ESP32 CSI ruview input.	451 KB	Easy
`parking-occupancy`	Per-zone parking occupancy via ESP32 CSI subcarrier-amplitude shift. Tracks utilization and churn-per-hour. Requires ruview.	451 KB	Easy
`queue-length`	Estimates line length and wait time	6 KB	Med
`shelf-engagement`	Detects when customers interact with products	6 KB	Med
`table-turnover`	Tracks which restaurant tables are free or occupied	4 KB	Easy

🏭 Industrial — _{7 modules}

ID	What it does	Size	Difficulty
`clean-room`	Enforces max headcount in controlled environments	4 KB	Easy
`confined-space`	Monitors workers in tight spaces for safety	5 KB	Med
`forklift-proximity`	Warns if a forklift gets too close to workers	10 KB	Hard
`livestock-monitor`	Monitors animals for distress, escape, or illness	6 KB	Med
`ppe-compliance`	Cog-composition layer: alerts when ruview-densepose detects presence in a restricted zone without an accompanying PPE-camera-cog confirmation vector.	387 KB	Easy
`slip-fall-zone`	Pre-fall risk detector. Fires when motion-variance drop, splash audio, and optional cautious-gait CSI all signal elevated slip risk.	451 KB	Easy
`structural-vibration`	Detects dangerous vibrations in buildings or machines	8 KB	Hard

🔬 Research — _{12 modules}

ID	What it does	Size	Difficulty
`emotion-detect`	Reads stress and calm from body language and breathing	10 KB	Hard
`energy-harvester`	Optimize solar and battery for off-grid seed deployment	6 KB	Med
`gesture-language`	Recognizes sign language gestures in real time	12 KB	Hard
`ghost-hunter`	Finds unexplained environmental anomalies — for fun	10 KB	Hard
`happiness-score`	Estimates well-being from movement and mood signals	8 KB	Med
`hyperbolic-space`	Maps data into curved space for tree-like structures	12 KB	Hard
`music-conductor`	Reads a conductor's gestures for tempo and dynamics	12 KB	Hard
`plant-growth`	Tracks plant growth rate and day/night cycles	8 KB	Med
`rain-detect`	Detects when rain starts, stops, and how heavy it is	6 KB	Med
`ruview-densepose`	Full body pose tracking from WiFi — no cameras needed	50 KB	Hard
`sound-classifier`	Identify sounds like glass break, alarm, or baby cry	16 KB	Hard
`time-crystal`	Experiments with repeating time-pattern symmetry	12 KB	Hard

🤖 Ai — _{15 modules}

ID	What it does	Size	Difficulty
`anomaly-attractor`	Learns what's normal and catches anything weird	10 KB	Hard
`cognitive-pipeline`	FastGRNN anomaly gate + SmolLM2 sparse-LLM inference for on-device Pi Zero 2W cognitive events	320 KB	Hard
`dtw-gesture-learn`	Teach custom hand gestures by showing examples	14 KB	Med
`ewc-lifelong`	Learns new things without forgetting old lessons	8 KB	Hard
`federated-learning`	Train AI across seeds without sharing raw data	18 KB	Hard
`goap-autonomy`	Plans and executes goals on its own	14 KB	Hard
`meta-adapt`	Automatically tunes itself for best performance	10 KB	Hard
`micro-hnsw`	Fast on-device fingerprinting and classification	12 KB	Med
`neural-trader`	Spot market patterns and trends from live data	20 KB	Hard
`pagerank-influence`	Finds the most influential person in a group	12 KB	Med
`pattern-sequence`	Detects daily routines and repeated habits	10 KB	Med
`rag-local`	Search your documents using AI — runs on the seed	14 KB	Med
`spiking-tracker`	Brain-inspired tracker that runs on tiny hardware	16 KB	Hard

🐝 Swarm — _{11 modules}

ID	What it does	Size	Difficulty
`swarm-backup-restore`	Auto-backup data to other seeds — one-click restore	8 KB	Easy
`swarm-cluster-monitor`	Live dashboard of every seed's health and status	6 KB	Easy
`swarm-consensus`	Seeds vote before making critical changes together	16 KB	Hard
`swarm-delta-sync`	Auto-sync data between seeds — only sends changes	8 KB	Med
`swarm-deploy`	Install or remove cogs on all seeds at once	10 KB	Med
`swarm-distributed-store`	Spread data across seeds and search them all at once	14 KB	Hard
`swarm-edge-orchestrator`	Manage all ESP32 sensor nodes from one place	14 KB	Hard
`swarm-load-balancer`	Spread queries across seeds so no single one overloads	10 KB	Med
`swarm-mesh-manager`	Find, connect, and monitor all seeds on your network	12 KB	Easy
`swarm-mqtt-bridge`	Share events between seeds over MQTT messaging	6 KB	Easy
`swarm-witness-federation`	Share tamper-proof audit trails across seeds	12 KB	Hard

📡 Signal — _{6 modules}

ID	What it does	Size	Difficulty
`coherence-gate`	Filters out noisy signals and keeps clean ones	8 KB	Med
`flash-attention`	Focuses sensing on specific areas for better accuracy	12 KB	Med
`optimal-transport`	Measures motion using shape-aware signal comparison	12 KB	Hard
`person-matching`	Tells apart multiple people in the same room	18 KB	Hard
`sparse-recovery`	Recovers missing signal data from partial readings	16 KB	Hard
`temporal-compress`	Shrinks old data to save memory without losing meaning	14 KB	Med

🌐 Network — _{1 modules}

ID	What it does	Size	Difficulty
`tailscale`	Reach the seed from anywhere via a private WireGuard mesh (Tailscale). Userspace mode — no root.	700 KB	Med

🛠️ Developer — _{7 modules}

ID	What it does	Size	Difficulty
`adversarial`	Detects tampered or spoofed sensor signals	4 KB	Easy
`coherence`	Monitors signal quality across multiple channels	4 KB	Easy
`gesture`	Core gesture recognition building block for cogs	6 KB	Med
`interference-search`	Searches many possibilities at once for fast answers	14 KB	Hard
`psycho-symbolic`	Reasons over knowledge graphs with multiple styles	16 KB	Hard
`quantum-coherence`	Quantum-inspired model for advanced signal states	16 KB	Hard
`self-healing-mesh`	Keeps sensor mesh running even when nodes drop out	14 KB	Hard

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).

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

	Why WiFi sensing wins	Traditional alternative
🔒	No video, no GDPR/HIPAA imaging rules	Cameras require consent, signage, data retention policies
🧱	Works through walls, shelving, debris	Cameras need line-of-sight per room
🌙	Works in total darkness	Cameras need IR or visible light
💰	$0-$8 per zone (existing WiFi or ESP32)	Camera systems: $200-$2,000 per zone
🔌	WiFi already deployed everywhere	PIR/radar sensors require new wiring per room

🏥 Everyday — Healthcare, retail, office, hospitality (commodity WiFi)

Use Case	What It Does	Hardware	Key Metric	Edge Module
Elderly care / assisted living	Fall detection, nighttime activity monitoring, breathing rate during sleep — no wearable compliance needed	1 ESP32-S3 per room ($8)	Fall alert <2s	Sleep Apnea, Gait Analysis
Hospital patient monitoring	Continuous breathing + heart rate for non-critical beds without wired sensors; nurse alert on anomaly	1-2 APs per ward	Breathing: 6-30 BPM	Respiratory Distress, Cardiac Arrhythmia
Emergency room triage	Automated occupancy count + wait-time estimation; detect patient distress (abnormal breathing) in waiting areas	Existing hospital WiFi	Occupancy accuracy >95%	Queue Length, Panic Motion
Retail occupancy & flow	Real-time foot traffic, dwell time by zone, queue length — no cameras, no opt-in, GDPR-friendly	Existing store WiFi + 1 ESP32	Dwell resolution ~1m	Customer Flow, Dwell Heatmap
Office space utilization	Which desks/rooms are actually occupied, meeting room no-shows, HVAC optimization based on real presence	Existing enterprise WiFi	Presence latency <1s	Meeting Room, HVAC Presence
Hotel & hospitality	Room occupancy without door sensors, minibar/bathroom usage patterns, energy savings on empty rooms	Existing hotel WiFi	15-30% HVAC savings	Energy Audit, Lighting Zones
Restaurants & food service	Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas	Existing WiFi	Queue wait ±30s	Table Turnover, Queue Length
Parking garages	Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers	Existing WiFi	Through-concrete walls	Loitering, Elevator Count

🏟️ Specialized — Events, fitness, education, civic (CSI-capable hardware)

Use Case	What It Does	Hardware	Key Metric	Edge Module
Smart home automation	Room-level presence triggers (lights, HVAC, music) that work through walls — no dead zones, no motion-sensor timeouts	2-3 ESP32-S3 nodes ($24)	Through-wall range ~5m	HVAC Presence, Lighting Zones
Fitness & sports	Rep counting, posture correction, breathing cadence during exercise — no wearable, no camera in locker rooms	3+ ESP32-S3 mesh	Pose: 17 keypoints	Breathing Sync, Gait Analysis
Childcare & schools	Naptime breathing monitoring, playground headcount, restricted-area alerts — privacy-safe for minors	2-4 ESP32-S3 per zone	Breathing: ±1 BPM	Sleep Apnea, Perimeter Breach
Event venues & concerts	Crowd density mapping, crush-risk detection via breathing compression, emergency evacuation flow tracking	Multi-AP mesh (4-8 APs)	Density per m²	Customer Flow, Panic Motion
Stadiums & arenas	Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling	Enterprise AP grid	15-20 per AP mesh	Dwell Heatmap, Queue Length
Houses of worship	Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking	Existing WiFi	Zone-level accuracy	Elevator Count, Energy Audit
Warehouse & logistics	Worker safety zones, forklift proximity alerts, occupancy in hazardous areas — works through shelving and pallets	Industrial AP mesh	Alert latency <500ms	Forklift Proximity, Confined Space
Civic infrastructure	Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies	Municipal WiFi + ESP32	Real-time headcount	Customer Flow, Loitering
Museums & galleries	Visitor flow heatmaps, exhibit dwell time, crowd bottleneck alerts — no cameras near artwork (flash/theft risk)	Existing WiFi	Zone dwell ±5s	Dwell Heatmap, Shelf Engagement

🤖 Robotics & Industrial — Autonomous systems, manufacturing, android spatial awareness

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.

Use Case	What It Does	Hardware	Key Metric	Edge Module
Cobot safety zones	Detect human presence near collaborative robots — auto-slow or stop before contact, even behind obstructions	2-3 ESP32-S3 per cell	Presence latency <100ms	Forklift Proximity, Perimeter Breach
Warehouse AMR navigation	Autonomous mobile robots sense humans around blind corners, through shelving racks — no LIDAR occlusion	ESP32 mesh along aisles	Through-shelf detection	Forklift Proximity, Loitering
Android / humanoid spatial awareness	Ambient human pose sensing for social robots — detect gestures, approach direction, and personal space without cameras always on	Onboard ESP32-S3 module	17-keypoint pose	Gesture Language, Emotion Detection
Manufacturing line monitoring	Worker presence at each station, ergonomic posture alerts, headcount for shift compliance — works through equipment	Industrial AP per zone	Pose + breathing	Confined Space, Gait Analysis
Construction site safety	Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount	Ruggedized ESP32 mesh	Alert <2s, through-dust	Panic Motion, Structural Vibration
Agricultural robotics	Detect farm workers near autonomous harvesters in dusty/foggy field conditions where cameras are unreliable	Weatherproof ESP32 nodes	Range ~10m open field	Forklift Proximity, Rain Detection
Drone landing zones	Verify landing area is clear of humans — WiFi sensing works in rain, dust, and low light where downward cameras fail	Ground ESP32 nodes	Presence: >95% accuracy	Perimeter Breach, Tailgating
Clean room monitoring	Personnel tracking without cameras (particle contamination risk from camera fans) — gown compliance via pose	Existing cleanroom WiFi	No particulate emission	Clean Room, Livestock Monitor

🔥 Extreme — Through-wall, disaster, defense, underground

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.

Use Case	What It Does	Hardware	Key Metric	Edge Module
Search & rescue (WiFi-Mat)	Detect survivors through rubble/debris via breathing signature, START triage color classification, 3D localization	Portable ESP32 mesh + laptop	Through 30cm concrete	Respiratory Distress, Seizure Detection
Firefighting	Locate occupants through smoke and walls before entry; breathing detection confirms life signs remotely	Portable mesh on truck	Works in zero visibility	Sleep Apnea, Panic Motion
Prison & secure facilities	Cell occupancy verification, distress detection (abnormal vitals), perimeter sensing — no camera blind spots	Dedicated AP infrastructure	24/7 vital signs	Cardiac Arrhythmia, Loitering
Military / tactical	Through-wall personnel detection, room clearing confirmation, hostage vital signs at standoff distance	Directional WiFi + custom FW	Range: 5m through wall	Perimeter Breach, Weapon Detection
Border & perimeter security	Detect human presence in tunnels, behind fences, in vehicles — passive sensing, no active illumination to reveal position	Concealed ESP32 mesh	Passive / covert	Perimeter Breach, Tailgating
Mining & underground	Worker presence in tunnels where GPS/cameras fail, breathing detection after collapse, headcount at safety points	Ruggedized ESP32 mesh	Through rock/earth	Confined Space, Respiratory Distress
Maritime & naval	Below-deck personnel tracking through steel bulkheads (limited range, requires tuning), man-overboard detection	Ship WiFi + ESP32	Through 1-2 bulkheads	Structural Vibration, Panic Motion
Wildlife research	Non-invasive animal activity monitoring in enclosures or dens — no light pollution, no visual disturbance	Weatherproof ESP32 nodes	Zero light emission	Livestock Monitor, Dream Stage

🧠 Self-Learning WiFi AI (ADR-024) — Adaptive recognition, self-optimization, and intelligent anomaly detection

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, and classifies activities using only WiFi (named person-identity is an experimental, data-gated research capability — see below, not a shipped feature)
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

What	How it works	Why it matters
Self-supervised learning	The model watches WiFi signals and teaches itself what "similar" and "different" look like, without any human-labeled data	Deploy anywhere — just plug in a WiFi sensor and wait 10 minutes
Room identification	Each room produces a distinct WiFi fingerprint pattern	Know which room someone is in without GPS or beacons
Anomaly detection	An unexpected person or event creates a fingerprint that doesn't match anything seen before	Automatic intrusion and fall detection as a free byproduct
Person re-identification (experimental, research)	A real per-channel similarity matcher (Soul Signature §3.6, `wifi-densepose-bfld`); measured result: on WiFi-only cardiac+respiratory channels alone two people are not separable (gap ~0.0005)	Honest research capability — named identity is not claimed and is data-gated on enrollment with the decisive AETHER/body-resonance channel. See #1021
Environment adaptation	MicroLoRA adapters (1,792 parameters per room) fine-tune the model for each new space	Adapts to a new room with minimal data — 93% less than retraining from scratch
Memory preservation	EWC++ regularization remembers what was learned during pretraining	Switching to a new task doesn't erase prior knowledge
Hard-negative mining	Training focuses on the most confusing examples to learn faster	Better accuracy with the same amount of training data

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

Mode	What you need	What you get
Self-Supervised	Just raw WiFi data	A model that understands WiFi signal structure
Supervised	WiFi data + body pose labels	Full pose tracking + environment fingerprints
Cross-Modal	WiFi data + camera footage	Fingerprints aligned with visual understanding

Fingerprint Index Types

Index	What it stores	Real-world use
`env_fingerprint`	Average room fingerprint	"Is this the kitchen or the bedroom?"
`activity_pattern`	Activity boundaries	"Is someone cooking, sleeping, or exercising?"
`temporal_baseline`	Normal conditions	"Something unusual just happened in this room"
`person_track`	Individual movement signatures	"Person A just entered the living room"

Model Size

Component	Parameters	Memory (on ESP32)
Transformer backbone	~28,000	28 KB
Embedding projection head	~25,000	25 KB
Per-room MicroLoRA adapter	~1,800	2 KB
Total	~55,000	55 KB (of 520 KB available)

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.

# 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

Document	Description
User Guide	Step-by-step guide: installation, first run, API usage, hardware setup, training
Build Guide	Building from source (Rust and Python)
Home Assistant + Matter Integration	Works with Home Assistant via MQTT auto-discovery + Works with Matter (Apple Home / Google Home / Alexa / SmartThings) — full entity catalog, 3 starter blueprints, Lovelace dashboards, privacy mode, threshold tuning (ADR-115).
BFLD — Beamforming Feedback Layer for Detection	New privacy-gated WiFi sensing layer that measures + structurally prevents identity leakage from 802.11ac/ax Beamforming Feedback Information. Three type-enforced invariants (raw BFI never exits node, identity embedding is in-RAM-only, cross-site correlation cryptographically impossible via per-site BLAKE3 keyed hash + daily rotation). Ships full operator surface (`BfldPipeline`, `BfldPipelineHandle`, the Soul Signature §3.6 per-channel matcher `EnrolledMatcher`/`SoulMatchOracle` — experimental; named identity is data-gated, measured as not-separable on WiFi-only channels alone), MQTT topic router + HA-DISCO + availability + LWT, 3 operator HA blueprints, two runnable examples, eclipse-mosquitto:2 CI service container. 327+ tests. ADR-118 umbrella + sub-ADRs 119/120/121/122/123. Research dossier: `docs/research/BFLD/` (11 files, 13,544 words).
SENSE-BRIDGE — rvagent MCP server	Dual-transport MCP server (`@ruvnet/rvagent`) bridging the RuView sensing stack to AI agents (Claude Code, Cursor, ruflo swarms). 6 tools wired: `ruview.presence.now`, `ruview.vitals.get_{breathing,heart_rate,all}`, `ruview.bfld.last_scan`, `ruview.bfld.subscribe`. stdio + Streamable HTTP (`POST /mcp`, Origin-validated, bearer-token auth, `127.0.0.1` bind). Full 20-tool Zod schema barrel + 5 RUVIEW-POLICY governance tools. 93 tests. ADR-124. Try: `npx @ruvnet/rvagent stdio`.
Semantic Primitives — Precision/Recall	Per-primitive F1 on the held-out paired-capture set: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting, bathroom, fall-risk, bed-exit, no-movement, multi-room.
Claude Code / Codex Plugin	The `ruview` plugin + marketplace — skills, `/ruview-*` commands, agents, and the Codex prompt mirror
Portable harness — `npx @ruvnet/ruview`	MetaHarness-minted, host-portable RuView operator harness — `ruview.*` MCP tools + the MEASURED-vs-CLAIMED honesty guardrail enforced in code (ADR-182). A lighter, multi-host companion to the in-repo plugin.
Architecture Decisions	182 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure)
Domain Models	8 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI, rvCSI) — bounded contexts, aggregates, domain events, and ubiquitous language
rvCSI — edge RF sensing runtime	Rust-first / TypeScript-accessible / hardware-abstracted CSI runtime: multi-source ingestion (incl. real nexmon_csi `.pcap` from a Raspberry Pi 5 / Pi 4 / Pi 3B+ — CYW43455 / BCM43455c0) → validation → DSP → typed events → RuVector RF memory (ADR-095, ADR-096, domain model). Now its own repo — `ruvnet/rvcsi` — vendored here under `vendor/rvcsi`; 9 `rvcsi-*` crates on crates.io, `@ruv/rvcsi` on npm, plus a Claude Code plugin.
Desktop App	WIP — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization
`ruview-swarm`	Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4/ArduPilot compatibility, Ruflo AI-agent integration
Medical Examples	Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable
Extended Documentation	Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog

ruvnet/RuView

项目说明

π RuView

See through walls with WiFi

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

Built for low-power edge applications

🤗 Pretrained model on Hugging Face

Results & proof

🧩 Edge Module Catalog

🫀 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}

🔬 How It Works

🏢 Use Cases & Applications

🧩 Claude Code & Codex Plugin

📖 Documentation

🚧 Beta software

📄 License

🤝 Creator Affiliate Program

📞 Support

ruvnet/RuView

项目说明

π RuView

See through walls with WiFi

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

Built for low-power edge applications

🤗 Pretrained model on Hugging Face

Results & proof

🧩 Edge Module Catalog

🫀 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

🔬 How It Works

🏢 Use Cases & Applications

🧩 Claude Code & Codex Plugin

📖 Documentation

🚧 Beta software

📄 License

🤝 Creator Affiliate Program

📞 Support

🫀 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}