GitHub - ruvnet/RuView: π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.

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 the full training pipeline for camera-free 17-keypoint pose estimation (WiFlow + AETHER + MERIDIAN heads) — based on the original DensePose From WiFi research at Carnegie Mellon University. What ships today is the inference and training infrastructure; pretrained pose weights are not yet released (tracked in #509). With no .rvf model loaded, the sensing server drives the on-screen skeleton from signal-based heuristics (amplitude variance, motion-band power), not learned keypoint inference. Camera-supervised fine-tune targets 35%+ PCK@20 (ADR-079) — pipeline implemented, P7–P9 (data collection + training + eval) are Pending. 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 indicator ⚠️ Heuristic, not learned Phase variance vs adaptive threshold (60 s ambient calibration).

False-positives under strong RF interference. < 1 ms latency

🚶 Motion / activity ✅ Works today Motion-band power + phase acceleration Real-time

🤸 Fall detection ✅ Works today Phase acceleration > threshold + 3-frame debounce + 5 s cooldown (#263) < 200 ms

🧮 Multi-person slot count ⚠️ Heuristic, not learned Subcarrier diversity divided by 2 (capped). Not a learned counter — see firmware README "Tier 2 caveats". Adaptive normalisation fix in #491. Real-time

🦴 17-keypoint pose estimation 🔬 Pipeline only, no shipped weights Training infrastructure complete (WiFlow + AETHER + MERIDIAN heads); pretrained .rvf not yet released. Fallback heuristic in the meantime. Tracked in #509. Pending data collection

🧱 Through-wall sensing ✅ Works today Fresnel zone geometry + multipath modeling Up to ~5m signal-dependent

🧠 Edge intelligence ✅ Works today Optional Cognitum Seed for persistent vector store + kNN + witness chain $140 total BOM

🎯 Camera-free pre-training ✅ Pipeline works MM-Fi + Wi-Pose datasets through wifi-densepose-train. Released weights pending #509. 84 s/epoch on M4 Pro

📷 Camera-supervised fine-tune 🔬 Pipeline only MediaPipe + ESP32 CSI paired training, ADR-079. Target 35%+ PCK@20. P7–P9 (data + train + eval) Pending. ~19 min/epoch on laptop

📡 Multi-frequency mesh ✅ Works today Channel hopping across 6 bands, TDM slot scheduling (ADR-029) 3x sensing bandwidth

🌐 3D point cloud fusion 🔬 Reference impl Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model.

Requires camera. 22 ms pipeline · 19K+ points/frame

Legend: ✅ shipped + tested on hardware · ⚠️ ships and runs, but is a heuristic/threshold (not a learned classifier) — accuracy depends on calibration · 🔬 implementation + tests in repo, weights/data/eval pending

# 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 NoteCSI-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 indicator, motion, breathing rate, heart rate, fall detection, slot-count multi-person heuristic + persistent vector store, kNN search, witness chain, MCP proxy. (Pose pending weights — see #509.)

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

Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables ▶ Live Observatory Demo  |  ▶ Dual-Modal Pose Fusion Demo  |  ▶ Live 3D Point Cloud 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.

🔬 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 <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