π RuView

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 — use camera ground-truth training for 92.9% PCK@20

Contributions and bug reports welcome at Issues.

See through walls with WiFi

Turn ordinary WiFi into a 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 also supports pose estimation (17 COCO keypoints via the WiFlow architecture), trained entirely without cameras using 10 sensor signals — a technique pioneered from the original DensePose From WiFi research at Carnegie Mellon University.

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
🦴 Pose estimation	CSI subcarrier amplitude/phase → 17 COCO keypoints	171K emb/s (M4 Pro)
🫁 Breathing detection	Bandpass 0.1-0.5 Hz → zero-crossing BPM	6-30 BPM
💓 Heart rate	Bandpass 0.8-2.0 Hz → zero-crossing BPM	40-120 BPM
👤 Presence sensing	Trained model + PIR fusion — 100% accuracy	0.012 ms latency
🧱 Through-wall	Fresnel zone geometry + multipath modeling	Up to 5m depth
🧠 Edge intelligence	8-dim feature vectors + RVF store on Cognitum Seed	$140 total BOM
🎯 Camera-free training	10 sensor signals, no labels needed	84s on M4 Pro
📷 Camera-supervised training	MediaPipe + ESP32 CSI → 92.9% PCK@20	19 min on laptop
📡 Multi-frequency mesh	Channel hopping across 6 bands, neighbor APs as illuminators	3x sensing bandwidth
🌐 3D point cloud (optional 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 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	Pose, breathing, heartbeat, motion, presence + persistent vector store, kNN search, witness chain, MCP proxy
ESP32 Mesh	3-6x ESP32-S3 + WiFi router	~$54	Yes	Pose, breathing, heartbeat, motion, presence
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

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	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

🧩 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	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

Module	File	What It Does	Budget
PageRank Influence	spt_pagerank_influence.rs	4x4 cross-correlation graph with power iteration PageRank	L (<2ms)
Micro HNSW	spt_micro_hnsw.rs	64-vector navigable small-world graph for nearest-neighbor search	S (<5ms)
Spiking Tracker	spt_spiking_tracker.rs	32 LIF neurons + 4 output zone neurons with STDP learning	S (<5ms)

⏱️ Temporal Analysis — Activity patterns, logic verification, autonomous planning

Module	File	What It Does	Budget
Pattern Sequence	tmp_pattern_sequence.rs	Activity routine detection and deviation alerts	S (<5ms)
Temporal Logic Guard	tmp_temporal_logic_guard.rs	LTL formula verification on CSI event streams	S (<5ms)
GOAP Autonomy	tmp_goap_autonomy.rs	Goal-Oriented Action Planning for autonomous module management	S (<5ms)

🛡️ AI Security — Tamper detection and behavioral anomaly profiling

Module	File	What It Does	Budget
Prompt Shield	ais_prompt_shield.rs	FNV-1a replay detection, injection detection (10x amplitude), jamming (SNR)	L (<2ms)
Behavioral Profiler	ais_behavioral_profiler.rs	6D behavioral profile with Mahalanobis anomaly scoring	S (<5ms)

⚛️ Quantum-Inspired — Quantum computing metaphors applied to CSI analysis

Module	File	What It Does	Budget
Quantum Coherence	qnt_quantum_coherence.rs	Bloch sphere mapping, Von Neumann entropy, decoherence detection	S (<5ms)
Interference Search	qnt_interference_search.rs	16 room-state hypotheses with Grover-inspired oracle + diffusion	S (<5ms)

🤖 Autonomous Systems — Self-governing and self-healing behaviors

Module	File	What It Does	Budget
Psycho-Symbolic	aut_psycho_symbolic.rs	16-rule forward-chaining knowledge base with contradiction detection	S (<5ms)
Self-Healing Mesh	aut_self_healing_mesh.rs	8-node mesh with health tracking, degradation/recovery, coverage healing	S (<5ms)

🔮 Exotic (Vendor) — Novel mathematical models for CSI interpretation

Module	File	What It Does	Budget
Time Crystal	exo_time_crystal.rs	Autocorrelation subharmonic detection in 256-frame history	S (<5ms)
Hyperbolic Space	exo_hyperbolic_space.rs	Poincare ball embedding with 32 reference locations, hyperbolic distance	S (<5ms)

🏥 Medical & Health (Category 1) — Contactless health monitoring

Module	File	What It Does	Budget
Sleep Apnea	med_sleep_apnea.rs	Detects breathing pauses during sleep	S (<5ms)
Cardiac Arrhythmia	med_cardiac_arrhythmia.rs	Monitors heart rate for irregular rhythms	S (<5ms)
Respiratory Distress	med_respiratory_distress.rs	Alerts on abnormal breathing patterns	S (<5ms)
Gait Analysis	med_gait_analysis.rs	Tracks walking patterns and detects changes	S (<5ms)
Seizure Detection	med_seizure_detect.rs	6-state machine for tonic-clonic seizure recognition	S (<5ms)

🔐 Security & Safety (Category 2) — Perimeter and threat detection

Module	File	What It Does	Budget
Perimeter Breach	sec_perimeter_breach.rs	Detects boundary crossings with approach/departure	S (<5ms)
Weapon Detection	sec_weapon_detect.rs	Metal anomaly detection via CSI amplitude shifts	S (<5ms)
Tailgating	sec_tailgating.rs	Detects unauthorized follow-through at access points	S (<5ms)
Loitering	sec_loitering.rs	Alerts when someone lingers too long in a zone	S (<5ms)
Panic Motion	sec_panic_motion.rs	Detects fleeing, struggling, or panic movement	S (<5ms)

🏢 Smart Building (Category 3) — Automation and energy efficiency

Module	File	What It Does	Budget
HVAC Presence	bld_hvac_presence.rs	Occupancy-driven HVAC control with departure countdown	S (<5ms)
Lighting Zones	bld_lighting_zones.rs	Auto-dim/off lighting based on zone activity	S (<5ms)
Elevator Count	bld_elevator_count.rs	Counts people entering/leaving with overload warning	S (<5ms)
Meeting Room	bld_meeting_room.rs	Tracks meeting lifecycle: start, headcount, end, availability	S (<5ms)
Energy Audit	bld_energy_audit.rs	Tracks after-hours usage and room utilization rates	S (<5ms)

🛒 Retail & Hospitality (Category 4) — Customer insights without cameras

Module	File	What It Does	Budget
Queue Length	ret_queue_length.rs	Estimates queue size and wait times	S (<5ms)
Dwell Heatmap	ret_dwell_heatmap.rs	Shows where people spend time (hot/cold zones)	S (<5ms)
Customer Flow	ret_customer_flow.rs	Counts ins/outs and tracks net occupancy	S (<5ms)
Table Turnover	ret_table_turnover.rs	Restaurant table lifecycle: seated, dining, vacated	S (<5ms)
Shelf Engagement	ret_shelf_engagement.rs	Detects browsing, considering, and reaching for products	S (<5ms)

🏭 Industrial & Specialized (Category 5) — Safety and compliance

Module	File	What It Does	Budget
Forklift Proximity	ind_forklift_proximity.rs	Warns when people get too close to vehicles	S (<5ms)
Confined Space	ind_confined_space.rs	OSHA-compliant worker monitoring with extraction alerts	S (<5ms)
Clean Room	ind_clean_room.rs	Occupancy limits and turbulent motion detection	S (<5ms)
Livestock Monitor	ind_livestock_monitor.rs	Animal presence, stillness, and escape alerts	S (<5ms)
Structural Vibration	ind_structural_vibration.rs	Seismic events, mechanical resonance, structural drift	S (<5ms)

🔮 Exotic & Research (Category 6) — Experimental sensing applications

Module	File	What It Does	Budget
Dream Stage	exo_dream_stage.rs	Contactless sleep stage classification (wake/light/deep/REM)	S (<5ms)
Emotion Detection	exo_emotion_detect.rs	Arousal, stress, and calm detection from micro-movements	S (<5ms)
Gesture Language	exo_gesture_language.rs	Sign language letter recognition via WiFi	S (<5ms)
Music Conductor	exo_music_conductor.rs	Tempo and dynamic tracking from conducting gestures	S (<5ms)
Plant Growth	exo_plant_growth.rs	Monitors plant growth, circadian rhythms, wilt detection	S (<5ms)
Ghost Hunter	exo_ghost_hunter.rs	Environmental anomaly classification (draft/insect/wind/unknown)	S (<5ms)
Rain Detection	exo_rain_detect.rs	Detects rain onset, intensity, and cessation via signal scatter	S (<5ms)
Breathing Sync	exo_breathing_sync.rs	Detects synchronized breathing between multiple people	S (<5ms)

---

🧠 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, 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

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	Each person disturbs WiFi in a slightly different way, creating a personal signature	Track individuals across sessions without cameras
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.

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📖 Documentation

Document	Description
User Guide	Step-by-step guide: installation, first run, API usage, hardware setup, training
Build Guide	Building from source (Rust and Python)
Architecture Decisions	79 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure)
Domain Models	7 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI) — bounded contexts, aggregates, domain events, and ubiquitous language
Desktop App	WIP — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization
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

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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.