RuView – See through walls with WiFi

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 ships pretrained CSI weights on Hugging Face at ruvnet/wifi-densepose-pretrained — a self-supervised contrastive CSI encoder (128-dim embeddings, 12.2M training steps, 60K frames) + a presence-detection head reporting 100% accuracy on the validation set + per-node LoRA adapters. Models are released as.safetensors, 4-bit/8-bit/2-bit quantized.bin (4 KB–16 KB), and a JSONL RVF container. The Python training and evaluation tooling consumes these today via safetensors. Pending wiring: the sensing-server's --model flag still expects binary RVF, so live-server consumption of the JSONL bundle is gated on a JSONL adapter (or a re-publish in binary RVF) — see Pretrained model on Hugging Face below for the workaround. Not yet released: a 17-keypoint pose-estimation model — training pipeline is implemented (WiFlow + AETHER + MERIDIAN heads) but camera-supervised fine-tune phases P7–P9 of ADR-079 are Pending, tracked in #509. The live sensing server therefore drives the on-screen output from signal-based DSP heuristics today. 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 Status How Speed 🫁 Breathing rate ✅ Works today Bandpass 0.1-0.5 Hz → zero-crossing BPM, circular variance on wrapped phase (#593) 6-30 BPM 💓 Heart rate ✅ Works today Bandpass 0.8-2.0 Hz → zero-crossing BPM 40-120 BPM (needs good SNR) 👤 Presence detection ✅ Heuristic in server · 🤗 Trained head on HF (loader wiring pending) Live server uses phase-variance vs adaptive threshold (60 s ambient calibration). A trained presence-head.json reporting 100% validation accuracy is published in ruvnet/wifi-densepose-pretrained but the sensing-server's --model loader only accepts binary RVF today — JSONL adapter pending. threshold + 3-frame debounce + 5 s cooldown (#263) flag binary RVF (RVFS magic) ⚠️ Loader does not yet accept the JSONL container 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. 🔬 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. 🏢 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. 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 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 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 🧩 Edge Intelligence (ADR-041) — 60 WASM modules across 13 categories, all implemented (609 tests) Small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response. Each module is a tiny WASM file (5-30 KB) that you upload to the device over-the-air. It reads WiFi signal data and makes decisions locally in under 10 ms. ADR-041 defines 60 modules across 13 categories — all 60 are implemented with 609 tests passing. Category Examples 🏥 Medical & Health Sleep apnea detection, cardiac arrhythmia, gait analysis, seizure detection 🔐 Security & Safety Intrusion detection, perimeter breach, loitering, panic motion 🏢 Smart Building Zone occupancy, HVAC control, elevator counting, meeting room tracking 🛒 Retail & Hospitality Queue length, dwell heatmaps, customer flow, table turnover 🏭 Industrial Forklift proximity, confined space monitoring, structural vibration 🔮 Exotic & Research Sleep staging, emotion detection, sign language, breathing sync 📡 Signal Intelligence Cleans and sharpens raw WiFi signals — focuses on important regions, filters noise, fills in missing data, and tracks which person is which 🧠 Adaptive Learning The sensor learns new gestures and patterns on its own over time — no cloud needed, remembers what it learned even after updates 🗺️ Spatial Reasoning Figures out where people are in a room, which zones matter most, and tracks movement across areas using graph-based spatial logic ⏱️ Temporal Analysis Learns daily routines, detects when patterns break (someone didn't get up), and verifies safety rules are being followed over time 🛡️ AI Security Detects signal replay attacks, WiFi jamming, injection attempts, and flags abnormal behavior that could indicate tampering ⚛️ Quantum-Inspired Uses quantum-inspired math to map room-wide signal coherence and search for optimal sensor configurations 🤖 Autonomous & Exotic Self-managing sensor mesh — auto-heals dropped nodes, plans its own actions, and explores experimental signal representations All implemented modules are no_std Rust, share a common utility library, and talk to the host through a 12-function API. Full documentation: Edge Modules Guide. See the complete implemented module list below. 🧩 Edge Intelligence — All 65 Modules Implemented (ADR-041 complete) All 60 modules are implemented, tested (609 tests passing), and ready to deploy. They compile to wasm32-unknown-unknown, run on ESP32-S3 via WASM3, and share a common utility library. Source: crates/wifi-densepose-wasm-edge/src/ Core modules (ADR-040 flagship + early implementations): Module File What It Does Gesture Classifier gesture.rs DTW template matching for hand gestures Coherence Filter coherence.rs Phase coherence gating for signal quality Adversarial Detector adversarial.rs Detects physically impossible signal patterns Intrusion Detector intrusion.rs Human vs non-human motion classification Occupancy Counter occupancy.rs Zone-level person counting Vital Trend.rs Long-term breathing and heart rate trending RVF Parser rvf.rs RVF container format parsing Vendor-integrated modules (24 modules, ADR-041 Category 7): 📡 Signal Intelligence — Real-time CSI analysis and feature extraction Module File What It Does Budget Flash Attention sig_flash_attention.rs Tiled attention over 8 subcarrier groups — finds spatial focus regions and entropy S (<5ms) Coherence Gate sig_coherence_gate.rs Z-score phasor gating with hysteresis: Accept / PredictOnly / Reject / Recalibrate L (<2ms) Temporal Compress sig_temporal_compress.rs 3-tier adaptive quantization (8-bit hot / 5-bit warm / 3-bit cold) L (<2ms) Sparse Recovery sig_sparse_recovery.rs ISTA L1 reconstruction for dropped subcarriers H (<10ms) Person Match sig_mincut_person_match.rs Hungarian-lite bipartite assignment for multi-person tracking S (<5ms) Optimal Transport sig_optimal_transport.rs Sliced Wasserstein-1 distance with 4 projections L (<2ms) 🧠 Adaptive Learning — On-device learning without cloud connectivity Module File What It Does Budget DTW Gesture Learn lrn_dtw_gesture_learn.rs User-teachable gesture recognition — 3-rehearsal protocol, 16 templates S (<5ms) Anomaly Attractor lrn_anomaly_attractor.rs 4D dynamical system attractor classification with Lyapunov exponents H (<10ms) Meta Adapt lrn_meta_adapt.rs Hill-climbing self-optimization with safety rollback L (<2ms) EWC Lifelong lrn_ewc_lifelong.rs Elastic Weight Consolidation — remembers past tasks while learning new ones S (<5ms) 🗺️ Spatial Reasoning — Location, proximity, and influence mapping