# RuView ยท Use Cases & Applications This file lists every concrete deployment scenario RuView is designed to serve, plus the 60-module ADR-041 Edge Intelligence catalogue and the ADR-024 self-learning system. Pulled out of the main README to keep the landing page short โ€” see [`../README.md`](../README.md) for the high-level overview and quick start. --- WiFi sensing works anywhere WiFi exists. No new hardware in most cases โ€” 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 โ€” 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 | |---|----------------------|----------------------| | ๐Ÿ”’ | **No video, no GDPR/HIPAA imaging rules** | Cameras require consent, signage, data retention policies | | ๐Ÿงฑ | **Works through walls, shelving, debris** | Cameras need line-of-sight per room | | ๐ŸŒ™ | **Works in total darkness** | Cameras need IR or visible light | | ๐Ÿ’ฐ | **$0-$8 per zone** (existing WiFi or ESP32) | Camera systems: $200-$2,000 per zone | | ๐Ÿ”Œ | **WiFi already deployed everywhere** | PIR/radar sensors require new wiring per room |
๐Ÿฅ Everyday โ€” 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 โ€” no wearable compliance needed | 1 ESP32-S3 per room ($8) | Fall alert <2s | [Sleep Apnea](edge-modules/medical.md), [Gait Analysis](edge-modules/medical.md) | | **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](edge-modules/medical.md), [Cardiac Arrhythmia](edge-modules/medical.md) | | **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](edge-modules/retail.md), [Panic Motion](edge-modules/security.md) | | **Retail occupancy & flow** | Real-time foot traffic, dwell time by zone, queue length โ€” no cameras, no opt-in, GDPR-friendly | Existing store WiFi + 1 ESP32 | Dwell resolution ~1m | [Customer Flow](edge-modules/retail.md), [Dwell Heatmap](edge-modules/retail.md) | | **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](edge-modules/building.md), [HVAC Presence](edge-modules/building.md) | | **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](edge-modules/building.md), [Lighting Zones](edge-modules/building.md) | | **Restaurants & food service** | Table turnover tracking, kitchen staff presence, restroom occupancy displays โ€” no cameras in dining areas | Existing WiFi | Queue wait ยฑ30s | [Table Turnover](edge-modules/retail.md), [Queue Length](edge-modules/retail.md) | | **Parking garages** | Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers | Existing WiFi | Through-concrete walls | [Loitering](edge-modules/security.md), [Elevator Count](edge-modules/building.md) |
๐ŸŸ๏ธ Specialized โ€” 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 โ€” no dead zones, no motion-sensor timeouts | 2-3 ESP32-S3 nodes ($24) | Through-wall range ~5m | [HVAC Presence](edge-modules/building.md), [Lighting Zones](edge-modules/building.md) | | **Fitness & sports** | Rep counting, posture correction, breathing cadence during exercise โ€” no wearable, no camera in locker rooms | 3+ ESP32-S3 mesh | Pose: 17 keypoints | [Breathing Sync](edge-modules/exotic.md), [Gait Analysis](edge-modules/medical.md) | | **Childcare & schools** | Naptime breathing monitoring, playground headcount, restricted-area alerts โ€” privacy-safe for minors | 2-4 ESP32-S3 per zone | Breathing: ยฑ1 BPM | [Sleep Apnea](edge-modules/medical.md), [Perimeter Breach](edge-modules/security.md) | | **Event venues & concerts** | Crowd density mapping, crush-risk detection via breathing compression, emergency evacuation flow tracking | Multi-AP mesh (4-8 APs) | Density per mยฒ | [Customer Flow](edge-modules/retail.md), [Panic Motion](edge-modules/security.md) | | **Stadiums & arenas** | Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling | Enterprise AP grid | 15-20 per AP mesh | [Dwell Heatmap](edge-modules/retail.md), [Queue Length](edge-modules/retail.md) | | **Houses of worship** | Attendance counting without facial recognition โ€” privacy-sensitive congregations, multi-room campus tracking | Existing WiFi | Zone-level accuracy | [Elevator Count](edge-modules/building.md), [Energy Audit](edge-modules/building.md) | | **Warehouse & logistics** | Worker safety zones, forklift proximity alerts, occupancy in hazardous areas โ€” works through shelving and pallets | Industrial AP mesh | Alert latency <500ms | [Forklift Proximity](edge-modules/industrial.md), [Confined Space](edge-modules/industrial.md) | | **Civic infrastructure** | Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies | Municipal WiFi + ESP32 | Real-time headcount | [Customer Flow](edge-modules/retail.md), [Loitering](edge-modules/security.md) | | **Museums & galleries** | Visitor flow heatmaps, exhibit dwell time, crowd bottleneck alerts โ€” no cameras near artwork (flash/theft risk) | Existing WiFi | Zone dwell ยฑ5s | [Dwell Heatmap](edge-modules/retail.md), [Shelf Engagement](edge-modules/retail.md) |
๐Ÿค– Robotics & Industrial โ€” Autonomous systems, manufacturing, android spatial awareness WiFi sensing gives robots and autonomous systems a spatial awareness layer that works where LIDAR and cameras fail โ€” 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 โ€” auto-slow or stop before contact, even behind obstructions | 2-3 ESP32-S3 per cell | Presence latency <100ms | [Forklift Proximity](edge-modules/industrial.md), [Perimeter Breach](edge-modules/security.md) | | **Warehouse AMR navigation** | Autonomous mobile robots sense humans around blind corners, through shelving racks โ€” no LIDAR occlusion | ESP32 mesh along aisles | Through-shelf detection | [Forklift Proximity](edge-modules/industrial.md), [Loitering](edge-modules/security.md) | | **Android / humanoid spatial awareness** | Ambient human pose sensing for social robots โ€” detect gestures, approach direction, and personal space without cameras always on | Onboard ESP32-S3 module | 17-keypoint pose | [Gesture Language](edge-modules/exotic.md), [Emotion Detection](edge-modules/exotic.md) | | **Manufacturing line monitoring** | Worker presence at each station, ergonomic posture alerts, headcount for shift compliance โ€” works through equipment | Industrial AP per zone | Pose + breathing | [Confined Space](edge-modules/industrial.md), [Gait Analysis](edge-modules/medical.md) | | **Construction site safety** | Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount | Ruggedized ESP32 mesh | Alert <2s, through-dust | [Panic Motion](edge-modules/security.md), [Structural Vibration](edge-modules/industrial.md) | | **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](edge-modules/industrial.md), [Rain Detection](edge-modules/exotic.md) | | **Drone landing zones** | Verify landing area is clear of humans โ€” WiFi sensing works in rain, dust, and low light where downward cameras fail | Ground ESP32 nodes | Presence: >95% accuracy | [Perimeter Breach](edge-modules/security.md), [Tailgating](edge-modules/security.md) | | **Clean room monitoring** | Personnel tracking without cameras (particle contamination risk from camera fans) โ€” gown compliance via pose | Existing cleanroom WiFi | No particulate emission | [Clean Room](edge-modules/industrial.md), [Livestock Monitor](edge-modules/industrial.md) |
๐Ÿ”ฅ Extreme โ€” Through-wall, disaster, defense, underground These scenarios exploit WiFi's ability to penetrate solid materials โ€” concrete, rubble, earth โ€” 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](edge-modules/medical.md), [Seizure Detection](edge-modules/medical.md) | | **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](edge-modules/medical.md), [Panic Motion](edge-modules/security.md) | | **Prison & secure facilities** | Cell occupancy verification, distress detection (abnormal vitals), perimeter sensing โ€” no camera blind spots | Dedicated AP infrastructure | 24/7 vital signs | [Cardiac Arrhythmia](edge-modules/medical.md), [Loitering](edge-modules/security.md) | | **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](edge-modules/security.md), [Weapon Detection](edge-modules/security.md) | | **Border & perimeter security** | Detect human presence in tunnels, behind fences, in vehicles โ€” passive sensing, no active illumination to reveal position | Concealed ESP32 mesh | Passive / covert | [Perimeter Breach](edge-modules/security.md), [Tailgating](edge-modules/security.md) | | **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](edge-modules/industrial.md), [Respiratory Distress](edge-modules/medical.md) | | **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](edge-modules/industrial.md), [Panic Motion](edge-modules/security.md) | | **Wildlife research** | Non-invasive animal activity monitoring in enclosures or dens โ€” no light pollution, no visual disturbance | Weatherproof ESP32 nodes | Zero light emission | [Livestock Monitor](edge-modules/industrial.md), [Dream Stage](edge-modules/exotic.md) |
๐Ÿงฉ Edge Intelligence (ADR-041) โ€” 60 WASM modules across 13 categories, all implemented (609 tests) Small programs that run directly on the ESP32 sensor โ€” 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](adr/ADR-041-wasm-module-collection.md) defines 60 modules across 13 categories โ€” all 60 are implemented with 609 tests passing. | | Category | Examples | |---|----------|---------| | ๐Ÿฅ | [**Medical & Health**](edge-modules/medical.md) | Sleep apnea detection, cardiac arrhythmia, gait analysis, seizure detection | | ๐Ÿ” | [**Security & Safety**](edge-modules/security.md) | Intrusion detection, perimeter breach, loitering, panic motion | | ๐Ÿข | [**Smart Building**](edge-modules/building.md) | Zone occupancy, HVAC control, elevator counting, meeting room tracking | | ๐Ÿ›’ | [**Retail & Hospitality**](edge-modules/retail.md) | Queue length, dwell heatmaps, customer flow, table turnover | | ๐Ÿญ | [**Industrial**](edge-modules/industrial.md) | Forklift proximity, confined space monitoring, structural vibration | | ๐Ÿ”ฎ | [**Exotic & Research**](edge-modules/exotic.md) | Sleep staging, emotion detection, sign language, breathing sync | | ๐Ÿ“ก | [**Signal Intelligence**](edge-modules/signal-intelligence.md) | Cleans and sharpens raw WiFi signals โ€” focuses on important regions, filters noise, fills in missing data, and tracks which person is which | | ๐Ÿง  | [**Adaptive Learning**](edge-modules/adaptive-learning.md) | The sensor learns new gestures and patterns on its own over time โ€” no cloud needed, remembers what it learned even after updates | | ๐Ÿ—บ๏ธ | [**Spatial Reasoning**](edge-modules/spatial-temporal.md) | Figures out where people are in a room, which zones matter most, and tracks movement across areas using graph-based spatial logic | | โฑ๏ธ | [**Temporal Analysis**](edge-modules/spatial-temporal.md) | Learns daily routines, detects when patterns break (someone didn't get up), and verifies safety rules are being followed over time | | ๐Ÿ›ก๏ธ | [**AI Security**](edge-modules/ai-security.md) | Detects signal replay attacks, WiFi jamming, injection attempts, and flags abnormal behavior that could indicate tampering | | โš›๏ธ | [**Quantum-Inspired**](edge-modules/autonomous.md) | Uses quantum-inspired math to map room-wide signal coherence and search for optimal sensor configurations | | ๐Ÿค– | [**Autonomous & Exotic**](edge-modules/autonomous.md) | Self-managing sensor mesh โ€” 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](../v2/crates/wifi-densepose-wasm-edge/src/vendor_common.rs), and talk to the host through a 12-function API. Full documentation: [**Edge Modules Guide**](edge-modules/README.md). See the [complete implemented module list](#edge-module-list) below.
๐Ÿงฉ Edge Intelligence โ€” 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](../v2/crates/wifi-densepose-wasm-edge/src/vendor_common.rs). Source: [`crates/wifi-densepose-wasm-edge/src/`](../v2/crates/wifi-densepose-wasm-edge/src/) **Core modules** (ADR-040 flagship + early implementations): | Module | File | What It Does | |--------|------|-------------| | Gesture Classifier | [`gesture.rs`](../v2/crates/wifi-densepose-wasm-edge/src/gesture.rs) | DTW template matching for hand gestures | | Coherence Filter | [`coherence.rs`](../v2/crates/wifi-densepose-wasm-edge/src/coherence.rs) | Phase coherence gating for signal quality | | Adversarial Detector | [`adversarial.rs`](../v2/crates/wifi-densepose-wasm-edge/src/adversarial.rs) | Detects physically impossible signal patterns | | Intrusion Detector | [`intrusion.rs`](../v2/crates/wifi-densepose-wasm-edge/src/intrusion.rs) | Human vs non-human motion classification | | Occupancy Counter | [`occupancy.rs`](../v2/crates/wifi-densepose-wasm-edge/src/occupancy.rs) | Zone-level person counting | | Vital Trend | [`vital_trend.rs`](../v2/crates/wifi-densepose-wasm-edge/src/vital_trend.rs) | Long-term breathing and heart rate trending | | RVF Parser | [`rvf.rs`](../v2/crates/wifi-densepose-wasm-edge/src/rvf.rs) | RVF container format parsing | **Vendor-integrated modules** (24 modules, ADR-041 Category 7): **๐Ÿ“ก Signal Intelligence** โ€” Real-time CSI analysis and feature extraction | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Flash Attention | [`sig_flash_attention.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sig_flash_attention.rs) | Tiled attention over 8 subcarrier groups โ€” finds spatial focus regions and entropy | S (<5ms) | | Coherence Gate | [`sig_coherence_gate.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sig_coherence_gate.rs) | Z-score phasor gating with hysteresis: Accept / PredictOnly / Reject / Recalibrate | L (<2ms) | | Temporal Compress | [`sig_temporal_compress.rs`](../v2/crates/wifi-densepose-wasm-edge/src/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`](../v2/crates/wifi-densepose-wasm-edge/src/sig_sparse_recovery.rs) | ISTA L1 reconstruction for dropped subcarriers | H (<10ms) | | Person Match | [`sig_mincut_person_match.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sig_mincut_person_match.rs) | Hungarian-lite bipartite assignment for multi-person tracking | S (<5ms) | | Optimal Transport | [`sig_optimal_transport.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sig_optimal_transport.rs) | Sliced Wasserstein-1 distance with 4 projections | L (<2ms) | **๐Ÿง  Adaptive Learning** โ€” On-device learning without cloud connectivity | Module | File | What It Does | Budget | |--------|------|-------------|--------| | DTW Gesture Learn | [`lrn_dtw_gesture_learn.rs`](../v2/crates/wifi-densepose-wasm-edge/src/lrn_dtw_gesture_learn.rs) | User-teachable gesture recognition โ€” 3-rehearsal protocol, 16 templates | S (<5ms) | | Anomaly Attractor | [`lrn_anomaly_attractor.rs`](../v2/crates/wifi-densepose-wasm-edge/src/lrn_anomaly_attractor.rs) | 4D dynamical system attractor classification with Lyapunov exponents | H (<10ms) | | Meta Adapt | [`lrn_meta_adapt.rs`](../v2/crates/wifi-densepose-wasm-edge/src/lrn_meta_adapt.rs) | Hill-climbing self-optimization with safety rollback | L (<2ms) | | EWC Lifelong | [`lrn_ewc_lifelong.rs`](../v2/crates/wifi-densepose-wasm-edge/src/lrn_ewc_lifelong.rs) | Elastic Weight Consolidation โ€” remembers past tasks while learning new ones | S (<5ms) | **๐Ÿ—บ๏ธ Spatial Reasoning** โ€” Location, proximity, and influence mapping | Module | File | What It Does | Budget | |--------|------|-------------|--------| | PageRank Influence | [`spt_pagerank_influence.rs`](../v2/crates/wifi-densepose-wasm-edge/src/spt_pagerank_influence.rs) | 4x4 cross-correlation graph with power iteration PageRank | L (<2ms) | | Micro HNSW | [`spt_micro_hnsw.rs`](../v2/crates/wifi-densepose-wasm-edge/src/spt_micro_hnsw.rs) | 64-vector navigable small-world graph for nearest-neighbor search | S (<5ms) | | Spiking Tracker | [`spt_spiking_tracker.rs`](../v2/crates/wifi-densepose-wasm-edge/src/spt_spiking_tracker.rs) | 32 LIF neurons + 4 output zone neurons with STDP learning | S (<5ms) | **โฑ๏ธ Temporal Analysis** โ€” Activity patterns, logic verification, autonomous planning | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Pattern Sequence | [`tmp_pattern_sequence.rs`](../v2/crates/wifi-densepose-wasm-edge/src/tmp_pattern_sequence.rs) | Activity routine detection and deviation alerts | S (<5ms) | | Temporal Logic Guard | [`tmp_temporal_logic_guard.rs`](../v2/crates/wifi-densepose-wasm-edge/src/tmp_temporal_logic_guard.rs) | LTL formula verification on CSI event streams | S (<5ms) | | GOAP Autonomy | [`tmp_goap_autonomy.rs`](../v2/crates/wifi-densepose-wasm-edge/src/tmp_goap_autonomy.rs) | Goal-Oriented Action Planning for autonomous module management | S (<5ms) | **๐Ÿ›ก๏ธ AI Security** โ€” Tamper detection and behavioral anomaly profiling | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Prompt Shield | [`ais_prompt_shield.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ais_prompt_shield.rs) | FNV-1a replay detection, injection detection (10x amplitude), jamming (SNR) | L (<2ms) | | Behavioral Profiler | [`ais_behavioral_profiler.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ais_behavioral_profiler.rs) | 6D behavioral profile with Mahalanobis anomaly scoring | S (<5ms) | **โš›๏ธ Quantum-Inspired** โ€” Quantum computing metaphors applied to CSI analysis | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Quantum Coherence | [`qnt_quantum_coherence.rs`](../v2/crates/wifi-densepose-wasm-edge/src/qnt_quantum_coherence.rs) | Bloch sphere mapping, Von Neumann entropy, decoherence detection | S (<5ms) | | Interference Search | [`qnt_interference_search.rs`](../v2/crates/wifi-densepose-wasm-edge/src/qnt_interference_search.rs) | 16 room-state hypotheses with Grover-inspired oracle + diffusion | S (<5ms) | **๐Ÿค– Autonomous Systems** โ€” Self-governing and self-healing behaviors | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Psycho-Symbolic | [`aut_psycho_symbolic.rs`](../v2/crates/wifi-densepose-wasm-edge/src/aut_psycho_symbolic.rs) | 16-rule forward-chaining knowledge base with contradiction detection | S (<5ms) | | Self-Healing Mesh | [`aut_self_healing_mesh.rs`](../v2/crates/wifi-densepose-wasm-edge/src/aut_self_healing_mesh.rs) | 8-node mesh with health tracking, degradation/recovery, coverage healing | S (<5ms) | **๐Ÿ”ฎ Exotic (Vendor)** โ€” Novel mathematical models for CSI interpretation | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Time Crystal | [`exo_time_crystal.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_time_crystal.rs) | Autocorrelation subharmonic detection in 256-frame history | S (<5ms) | | Hyperbolic Space | [`exo_hyperbolic_space.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_hyperbolic_space.rs) | Poincare ball embedding with 32 reference locations, hyperbolic distance | S (<5ms) | **๐Ÿฅ Medical & Health** (Category 1) โ€” Contactless health monitoring | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Sleep Apnea | [`med_sleep_apnea.rs`](../v2/crates/wifi-densepose-wasm-edge/src/med_sleep_apnea.rs) | Detects breathing pauses during sleep | S (<5ms) | | Cardiac Arrhythmia | [`med_cardiac_arrhythmia.rs`](../v2/crates/wifi-densepose-wasm-edge/src/med_cardiac_arrhythmia.rs) | Monitors heart rate for irregular rhythms | S (<5ms) | | Respiratory Distress | [`med_respiratory_distress.rs`](../v2/crates/wifi-densepose-wasm-edge/src/med_respiratory_distress.rs) | Alerts on abnormal breathing patterns | S (<5ms) | | Gait Analysis | [`med_gait_analysis.rs`](../v2/crates/wifi-densepose-wasm-edge/src/med_gait_analysis.rs) | Tracks walking patterns and detects changes | S (<5ms) | | Seizure Detection | [`med_seizure_detect.rs`](../v2/crates/wifi-densepose-wasm-edge/src/med_seizure_detect.rs) | 6-state machine for tonic-clonic seizure recognition | S (<5ms) | **๐Ÿ” Security & Safety** (Category 2) โ€” Perimeter and threat detection | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Perimeter Breach | [`sec_perimeter_breach.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sec_perimeter_breach.rs) | Detects boundary crossings with approach/departure | S (<5ms) | | Weapon Detection | [`sec_weapon_detect.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sec_weapon_detect.rs) | Metal anomaly detection via CSI amplitude shifts | S (<5ms) | | Tailgating | [`sec_tailgating.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sec_tailgating.rs) | Detects unauthorized follow-through at access points | S (<5ms) | | Loitering | [`sec_loitering.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sec_loitering.rs) | Alerts when someone lingers too long in a zone | S (<5ms) | | Panic Motion | [`sec_panic_motion.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sec_panic_motion.rs) | Detects fleeing, struggling, or panic movement | S (<5ms) | **๐Ÿข Smart Building** (Category 3) โ€” Automation and energy efficiency | Module | File | What It Does | Budget | |--------|------|-------------|--------| | HVAC Presence | [`bld_hvac_presence.rs`](../v2/crates/wifi-densepose-wasm-edge/src/bld_hvac_presence.rs) | Occupancy-driven HVAC control with departure countdown | S (<5ms) | | Lighting Zones | [`bld_lighting_zones.rs`](../v2/crates/wifi-densepose-wasm-edge/src/bld_lighting_zones.rs) | Auto-dim/off lighting based on zone activity | S (<5ms) | | Elevator Count | [`bld_elevator_count.rs`](../v2/crates/wifi-densepose-wasm-edge/src/bld_elevator_count.rs) | Counts people entering/leaving with overload warning | S (<5ms) | | Meeting Room | [`bld_meeting_room.rs`](../v2/crates/wifi-densepose-wasm-edge/src/bld_meeting_room.rs) | Tracks meeting lifecycle: start, headcount, end, availability | S (<5ms) | | Energy Audit | [`bld_energy_audit.rs`](../v2/crates/wifi-densepose-wasm-edge/src/bld_energy_audit.rs) | Tracks after-hours usage and room utilization rates | S (<5ms) | **๐Ÿ›’ Retail & Hospitality** (Category 4) โ€” Customer insights without cameras | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Queue Length | [`ret_queue_length.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ret_queue_length.rs) | Estimates queue size and wait times | S (<5ms) | | Dwell Heatmap | [`ret_dwell_heatmap.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ret_dwell_heatmap.rs) | Shows where people spend time (hot/cold zones) | S (<5ms) | | Customer Flow | [`ret_customer_flow.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ret_customer_flow.rs) | Counts ins/outs and tracks net occupancy | S (<5ms) | | Table Turnover | [`ret_table_turnover.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ret_table_turnover.rs) | Restaurant table lifecycle: seated, dining, vacated | S (<5ms) | | Shelf Engagement | [`ret_shelf_engagement.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ret_shelf_engagement.rs) | Detects browsing, considering, and reaching for products | S (<5ms) | **๐Ÿญ Industrial & Specialized** (Category 5) โ€” Safety and compliance | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Forklift Proximity | [`ind_forklift_proximity.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ind_forklift_proximity.rs) | Warns when people get too close to vehicles | S (<5ms) | | Confined Space | [`ind_confined_space.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ind_confined_space.rs) | OSHA-compliant worker monitoring with extraction alerts | S (<5ms) | | Clean Room | [`ind_clean_room.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ind_clean_room.rs) | Occupancy limits and turbulent motion detection | S (<5ms) | | Livestock Monitor | [`ind_livestock_monitor.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ind_livestock_monitor.rs) | Animal presence, stillness, and escape alerts | S (<5ms) | | Structural Vibration | [`ind_structural_vibration.rs`](../v2/crates/wifi-densepose-wasm-edge/src/ind_structural_vibration.rs) | Seismic events, mechanical resonance, structural drift | S (<5ms) | **๐Ÿ”ฎ Exotic & Research** (Category 6) โ€” Experimental sensing applications | Module | File | What It Does | Budget | |--------|------|-------------|--------| | Dream Stage | [`exo_dream_stage.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_dream_stage.rs) | Contactless sleep stage classification (wake/light/deep/REM) | S (<5ms) | | Emotion Detection | [`exo_emotion_detect.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_emotion_detect.rs) | Arousal, stress, and calm detection from micro-movements | S (<5ms) | | Gesture Language | [`exo_gesture_language.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_gesture_language.rs) | Sign language letter recognition via WiFi | S (<5ms) | | Music Conductor | [`exo_music_conductor.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_music_conductor.rs) | Tempo and dynamic tracking from conducting gestures | S (<5ms) | | Plant Growth | [`exo_plant_growth.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_plant_growth.rs) | Monitors plant growth, circadian rhythms, wilt detection | S (<5ms) | | Ghost Hunter | [`exo_ghost_hunter.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_ghost_hunter.rs) | Environmental anomaly classification (draft/insect/wind/unknown) | S (<5ms) | | Rain Detection | [`exo_rain_detect.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_rain_detect.rs) | Detects rain onset, intensity, and cessation via signal scatter | S (<5ms) | | Breathing Sync | [`exo_breathing_sync.rs`](../v2/crates/wifi-densepose-wasm-edge/src/exo_breathing_sync.rs) | Detects synchronized breathing between multiple people | S (<5ms) |
---
๐Ÿง  Self-Learning WiFi AI (ADR-024) โ€” 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 โ€” 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 โ€” 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 โ€” 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 โ€” 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] โ†’ Transformer + Graph Neural Network โ”œโ†’ 128-dim environment fingerprint (for search + identification) โ””โ†’ 17-joint body pose (for human tracking) ``` **Quick Start** ```bash # 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 โ€” extract fingerprints from live WiFi cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed # Step 4: Search โ€” 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 โ€” attention, graph algorithms, and compression โ€” adding contrastive learning on top. See [`architecture.md`](architecture.md) for the full pipeline. See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.