fix(docs): repair internal links broken by README/CLAUDE doc-slim
After ADR-117/118 docs sweep (commit 4075b608) extracted Use Cases,
How It Works, Edge Modules, Self-Learning sections from README into
docs/use-cases.md + docs/architecture.md, but two classes of links
were left dangling:
1. README anchor links pointing at section IDs that no longer exist
in README:
#edge-intelligence-adr-041 → moved to docs/use-cases.md
#esp32-s3-hardware-pipeline → architecture detail in docs/
#vital-sign-detection → moved out
#sensing-server → moved out
#-quick-start → renamed during slim
Replaced with deep links into docs/use-cases.md or docs/dev-handbook.md
/ docs/architecture.md where appropriate.
2. Extracted docs (docs/use-cases.md etc.) had path links written from
the perspective of repo root (docs/edge-modules/, v2/crates/...) —
broken once the file moved into docs/. Bulk-rewrote via Python
regex pass:
docs/edge-modules/X → edge-modules/X
docs/adr/X → adr/X
v2/... → ../v2/...
archive/... → ../archive/...
scripts/... → ../scripts/...
plugins/... → ../plugins/...
firmware/... → ../firmware/...
3. docs/use-cases.md self-reference #ai-backbone-ruvector → that
section was never moved; replaced with prose + link to
architecture.md.
Final scan: ZERO dangling anchors in the doc tree. One valid
anchor `#edge-module-list` in use-cases.md points to a local
`<details id="edge-module-list">` block.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
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README.md
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README.md
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@ -36,14 +36,14 @@ RuView also supports pose estimation (17 COCO keypoints via the WiFlow architect
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### Built for low-power edge applications
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[Edge modules](#edge-intelligence-adr-041) are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.
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[Edge modules](docs/use-cases.md) are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.
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[](https://www.rust-lang.org/)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/ruvnet/RuView)
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[](https://hub.docker.com/r/ruvnet/wifi-densepose)
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[](#vital-sign-detection)
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[](#esp32-s3-hardware-pipeline)
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[](docs/use-cases.md)
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[](docs/use-cases.md)
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[](https://crates.io/crates/wifi-densepose-ruvector)
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@ -109,9 +109,9 @@ node scripts/mincut-person-counter.js --port 5006 # Correct person counting
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<a href="https://ruvnet.github.io/RuView/pointcloud/"><strong>▶ Live 3D Point Cloud</strong></a>
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> The [server](#-quick-start) is optional for visualization and aggregation — the ESP32 [runs independently](#esp32-s3-hardware-pipeline) for presence detection, vital signs, and fall alerts.
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> The [server](docs/dev-handbook.md) is optional for visualization and aggregation — the ESP32 [runs independently](docs/architecture.md) for presence detection, vital signs, and fall alerts.
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>
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> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](#sensing-server) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).
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> **Live ESP32 pipeline**: Connect an ESP32-S3 node → run the [sensing server](docs/dev-handbook.md) → open the [pose fusion demo](https://ruvnet.github.io/RuView/pose-fusion.html) for real-time dual-modal pose estimation (webcam + WiFi CSI). See [ADR-059](docs/adr/ADR-059-live-esp32-csi-pipeline.md).
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## 🔬 How It Works
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@ -30,5 +30,5 @@ Neural Network: processed signals → 17 body keypoints + vital signs + room mod
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Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts
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```
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No training cameras required — the [Self-Learning system (ADR-024)](docs/adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](docs/adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.
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No training cameras required — the [Self-Learning system (ADR-024)](adr/ADR-024-contrastive-csi-embedding-model.md) bootstraps from raw WiFi data alone. [MERIDIAN (ADR-027)](adr/ADR-027-cross-environment-domain-generalization.md) ensures the model works in any room, not just the one it trained in.
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@ -26,14 +26,14 @@ WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just
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| Use Case | What It Does | Hardware | Key Metric | Edge Module |
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|----------|-------------|----------|------------|-------------|
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| **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](docs/edge-modules/medical.md), [Gait Analysis](docs/edge-modules/medical.md) |
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| **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](docs/edge-modules/medical.md), [Cardiac Arrhythmia](docs/edge-modules/medical.md) |
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| **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](docs/edge-modules/retail.md), [Panic Motion](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/retail.md), [Dwell Heatmap](docs/edge-modules/retail.md) |
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| **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](docs/edge-modules/building.md), [HVAC Presence](docs/edge-modules/building.md) |
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| **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](docs/edge-modules/building.md), [Lighting Zones](docs/edge-modules/building.md) |
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| **Restaurants & food service** | Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas | Existing WiFi | Queue wait ±30s | [Table Turnover](docs/edge-modules/retail.md), [Queue Length](docs/edge-modules/retail.md) |
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| **Parking garages** | Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers | Existing WiFi | Through-concrete walls | [Loitering](docs/edge-modules/security.md), [Elevator Count](docs/edge-modules/building.md) |
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| **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](edge-modules/medical.md), [Gait Analysis](edge-modules/medical.md) |
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| **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) |
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| **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) |
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| **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](edge-modules/retail.md), [Dwell Heatmap](edge-modules/retail.md) |
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| **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) |
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| **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) |
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| **Restaurants & food service** | Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas | Existing WiFi | Queue wait ±30s | [Table Turnover](edge-modules/retail.md), [Queue Length](edge-modules/retail.md) |
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| **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) |
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</details>
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@ -42,15 +42,15 @@ WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just
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| Use Case | What It Does | Hardware | Key Metric | Edge Module |
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|----------|-------------|----------|------------|-------------|
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| **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](docs/edge-modules/building.md), [Lighting Zones](docs/edge-modules/building.md) |
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| **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](docs/edge-modules/exotic.md), [Gait Analysis](docs/edge-modules/medical.md) |
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| **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](docs/edge-modules/medical.md), [Perimeter Breach](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/retail.md), [Panic Motion](docs/edge-modules/security.md) |
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| **Stadiums & arenas** | Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling | Enterprise AP grid | 15-20 per AP mesh | [Dwell Heatmap](docs/edge-modules/retail.md), [Queue Length](docs/edge-modules/retail.md) |
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| **Houses of worship** | Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking | Existing WiFi | Zone-level accuracy | [Elevator Count](docs/edge-modules/building.md), [Energy Audit](docs/edge-modules/building.md) |
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| **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](docs/edge-modules/industrial.md), [Confined Space](docs/edge-modules/industrial.md) |
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| **Civic infrastructure** | Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies | Municipal WiFi + ESP32 | Real-time headcount | [Customer Flow](docs/edge-modules/retail.md), [Loitering](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/retail.md), [Shelf Engagement](docs/edge-modules/retail.md) |
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| **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](edge-modules/building.md), [Lighting Zones](edge-modules/building.md) |
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| **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](edge-modules/exotic.md), [Gait Analysis](edge-modules/medical.md) |
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| **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](edge-modules/medical.md), [Perimeter Breach](edge-modules/security.md) |
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| **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](edge-modules/retail.md), [Panic Motion](edge-modules/security.md) |
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| **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) |
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| **Houses of worship** | Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking | Existing WiFi | Zone-level accuracy | [Elevator Count](edge-modules/building.md), [Energy Audit](edge-modules/building.md) |
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| **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](edge-modules/industrial.md), [Confined Space](edge-modules/industrial.md) |
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| **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) |
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| **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](edge-modules/retail.md), [Shelf Engagement](edge-modules/retail.md) |
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</details>
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| Use Case | What It Does | Hardware | Key Metric | Edge Module |
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|----------|-------------|----------|------------|-------------|
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| **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](docs/edge-modules/industrial.md), [Perimeter Breach](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/industrial.md), [Loitering](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/exotic.md), [Emotion Detection](docs/edge-modules/exotic.md) |
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| **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](docs/edge-modules/industrial.md), [Gait Analysis](docs/edge-modules/medical.md) |
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| **Construction site safety** | Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount | Ruggedized ESP32 mesh | Alert <2s, through-dust | [Panic Motion](docs/edge-modules/security.md), [Structural Vibration](docs/edge-modules/industrial.md) |
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| **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](docs/edge-modules/industrial.md), [Rain Detection](docs/edge-modules/exotic.md) |
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| **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](docs/edge-modules/security.md), [Tailgating](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/industrial.md), [Livestock Monitor](docs/edge-modules/industrial.md) |
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| **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](edge-modules/industrial.md), [Perimeter Breach](edge-modules/security.md) |
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| **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](edge-modules/industrial.md), [Loitering](edge-modules/security.md) |
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| **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](edge-modules/exotic.md), [Emotion Detection](edge-modules/exotic.md) |
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| **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](edge-modules/industrial.md), [Gait Analysis](edge-modules/medical.md) |
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| **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) |
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| **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) |
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| **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](edge-modules/security.md), [Tailgating](edge-modules/security.md) |
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| **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](edge-modules/industrial.md), [Livestock Monitor](edge-modules/industrial.md) |
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</details>
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@ -79,58 +79,58 @@ These scenarios exploit WiFi's ability to penetrate solid materials — concrete
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| Use Case | What It Does | Hardware | Key Metric | Edge Module |
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|----------|-------------|----------|------------|-------------|
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| **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](docs/edge-modules/medical.md), [Seizure Detection](docs/edge-modules/medical.md) |
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| **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](docs/edge-modules/medical.md), [Panic Motion](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/medical.md), [Loitering](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/security.md), [Weapon Detection](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/security.md), [Tailgating](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/industrial.md), [Respiratory Distress](docs/edge-modules/medical.md) |
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| **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](docs/edge-modules/industrial.md), [Panic Motion](docs/edge-modules/security.md) |
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| **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](docs/edge-modules/industrial.md), [Dream Stage](docs/edge-modules/exotic.md) |
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| **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) |
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| **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) |
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| **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](edge-modules/medical.md), [Loitering](edge-modules/security.md) |
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| **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) |
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| **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](edge-modules/security.md), [Tailgating](edge-modules/security.md) |
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| **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) |
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| **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) |
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| **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](edge-modules/industrial.md), [Dream Stage](edge-modules/exotic.md) |
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</details>
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<details>
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<summary><strong>🧩 Edge Intelligence (<a href="docs/adr/ADR-041-wasm-module-collection.md">ADR-041</a>)</strong> — 60 WASM modules across 13 categories, all implemented (609 tests)</summary>
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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](docs/adr/ADR-041-wasm-module-collection.md) defines 60 modules across 13 categories — all 60 are implemented with 609 tests passing.
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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](adr/ADR-041-wasm-module-collection.md) defines 60 modules across 13 categories — all 60 are implemented with 609 tests passing.
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| | Category | Examples |
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|---|----------|---------|
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| 🏥 | [**Medical & Health**](docs/edge-modules/medical.md) | Sleep apnea detection, cardiac arrhythmia, gait analysis, seizure detection |
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| 🔐 | [**Security & Safety**](docs/edge-modules/security.md) | Intrusion detection, perimeter breach, loitering, panic motion |
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| 🏢 | [**Smart Building**](docs/edge-modules/building.md) | Zone occupancy, HVAC control, elevator counting, meeting room tracking |
|
||||
| 🛒 | [**Retail & Hospitality**](docs/edge-modules/retail.md) | Queue length, dwell heatmaps, customer flow, table turnover |
|
||||
| 🏭 | [**Industrial**](docs/edge-modules/industrial.md) | Forklift proximity, confined space monitoring, structural vibration |
|
||||
| 🔮 | [**Exotic & Research**](docs/edge-modules/exotic.md) | Sleep staging, emotion detection, sign language, breathing sync |
|
||||
| 📡 | [**Signal Intelligence**](docs/edge-modules/signal-intelligence.md) | Cleans and sharpens raw WiFi signals — focuses on important regions, filters noise, fills in missing data, and tracks which person is which |
|
||||
| 🧠 | [**Adaptive Learning**](docs/edge-modules/adaptive-learning.md) | The sensor learns new gestures and patterns on its own over time — no cloud needed, remembers what it learned even after updates |
|
||||
| 🗺️ | [**Spatial Reasoning**](docs/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 |
|
||||
| ⏱️ | [**Temporal Analysis**](docs/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 |
|
||||
| 🛡️ | [**AI Security**](docs/edge-modules/ai-security.md) | Detects signal replay attacks, WiFi jamming, injection attempts, and flags abnormal behavior that could indicate tampering |
|
||||
| ⚛️ | [**Quantum-Inspired**](docs/edge-modules/autonomous.md) | Uses quantum-inspired math to map room-wide signal coherence and search for optimal sensor configurations |
|
||||
| 🤖 | [**Autonomous & Exotic**](docs/edge-modules/autonomous.md) | Self-managing sensor mesh — auto-heals dropped nodes, plans its own actions, and explores experimental signal representations |
|
||||
| 🏥 | [**Medical & Health**](edge-modules/medical.md) | Sleep apnea detection, cardiac arrhythmia, gait analysis, seizure detection |
|
||||
| 🔐 | [**Security & Safety**](edge-modules/security.md) | Intrusion detection, perimeter breach, loitering, panic motion |
|
||||
| 🏢 | [**Smart Building**](edge-modules/building.md) | Zone occupancy, HVAC control, elevator counting, meeting room tracking |
|
||||
| 🛒 | [**Retail & Hospitality**](edge-modules/retail.md) | Queue length, dwell heatmaps, customer flow, table turnover |
|
||||
| 🏭 | [**Industrial**](edge-modules/industrial.md) | Forklift proximity, confined space monitoring, structural vibration |
|
||||
| 🔮 | [**Exotic & Research**](edge-modules/exotic.md) | Sleep staging, emotion detection, sign language, breathing sync |
|
||||
| 📡 | [**Signal Intelligence**](edge-modules/signal-intelligence.md) | Cleans and sharpens raw WiFi signals — focuses on important regions, filters noise, fills in missing data, and tracks which person is which |
|
||||
| 🧠 | [**Adaptive Learning**](edge-modules/adaptive-learning.md) | The sensor learns new gestures and patterns on its own over time — no cloud needed, remembers what it learned even after updates |
|
||||
| 🗺️ | [**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 |
|
||||
| ⏱️ | [**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 |
|
||||
| 🛡️ | [**AI Security**](edge-modules/ai-security.md) | Detects signal replay attacks, WiFi jamming, injection attempts, and flags abnormal behavior that could indicate tampering |
|
||||
| ⚛️ | [**Quantum-Inspired**](edge-modules/autonomous.md) | Uses quantum-inspired math to map room-wide signal coherence and search for optimal sensor configurations |
|
||||
| 🤖 | [**Autonomous & Exotic**](edge-modules/autonomous.md) | 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](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**](docs/edge-modules/README.md). See the [complete implemented module list](#edge-module-list) below.
|
||||
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.
|
||||
|
||||
</details>
|
||||
|
||||
<details id="edge-module-list">
|
||||
<summary><strong>🧩 Edge Intelligence — <a href="docs/edge-modules/README.md">All 65 Modules Implemented</a></strong> (ADR-041 complete)</summary>
|
||||
|
||||
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/)
|
||||
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 |
|
||||
| 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):
|
||||
|
||||
|
|
@ -138,128 +138,128 @@ All 60 modules are implemented, tested (609 tests passing), and ready to deploy.
|
|||
|
||||
| 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 — 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) |
|
||||
| Flash Attention | [`sig_flash_attention.rs`](../v2/crates/wifi-densepose-wasm-edge/src/sig_flash_attention.rs) | Tiled attention over 8 subcarrier groups — 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) |
|
||||
|
||||
**🧠 Adaptive Learning** — 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 — 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 — remembers past tasks while learning new ones | S (<5ms) |
|
||||
| DTW Gesture Learn | [`lrn_dtw_gesture_learn.rs`](../v2/crates/wifi-densepose-wasm-edge/src/lrn_dtw_gesture_learn.rs) | User-teachable gesture recognition — 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 — 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`](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) |
|
||||
| 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) |
|
||||
|
||||
**⏱️ Temporal Analysis** — 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) |
|
||||
| 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) |
|
||||
|
||||
**🛡️ AI Security** — 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) |
|
||||
| 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) |
|
||||
|
||||
**⚛️ Quantum-Inspired** — 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) |
|
||||
| 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) |
|
||||
|
||||
**🤖 Autonomous Systems** — 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) |
|
||||
| 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) |
|
||||
|
||||
**🔮 Exotic (Vendor)** — 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) |
|
||||
| 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) |
|
||||
|
||||
**🏥 Medical & Health** (Category 1) — 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) |
|
||||
| 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) |
|
||||
|
||||
**🔐 Security & Safety** (Category 2) — 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) |
|
||||
| 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) |
|
||||
|
||||
**🏢 Smart Building** (Category 3) — 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) |
|
||||
| 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) |
|
||||
|
||||
**🛒 Retail & Hospitality** (Category 4) — 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) |
|
||||
| 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) |
|
||||
|
||||
**🏭 Industrial & Specialized** (Category 5) — 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) |
|
||||
| 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) |
|
||||
|
||||
**🔮 Exotic & Research** (Category 6) — 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) |
|
||||
| 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) |
|
||||
|
||||
</details>
|
||||
|
||||
|
|
@ -339,9 +339,9 @@ cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index en
|
|||
| 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)](#ai-backbone-ruvector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top.
|
||||
The self-learning system builds on the AI Backbone (RuVector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top. See [`architecture.md`](architecture.md) for the full pipeline.
|
||||
|
||||
See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.
|
||||
See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](adr/ADR-024-contrastive-csi-embedding-model.md) for full architectural details.
|
||||
|
||||
</details>
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue