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wifi-densepose/plugins/ruview/skills/ruview-applications/SKILL.md

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name description allowed-tools
ruview-applications Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually *do* something with a working RuView setup. Bash Read Write Edit Glob Grep

RuView Applications

What RuView can sense, and how to run each one. Assumes you have either the Docker demo (simulated CSI) or a live ESP32 sink (see ruview-quickstart / ruview-hardware-setup).

Application catalogue

Application What it does Entry point
Presence / occupancy Detect people through walls, count them, track entries/exits (trained model + PIR fusion, ~0.012 ms latency) sensing-server live mode; examples/environment/
Vital signs Breathing 6–30 BPM (bandpass 0.1–0.5 Hz), heart rate 40–120 BPM (bandpass 0.8–2.0 Hz), contactless while sleeping/sitting wifi-densepose-vitals crate (ADR-021); examples/medical/
Activity recognition Walking, sitting, gestures, falls — from temporal CSI patterns RuvSense gesture.rs (DTW), pose_tracker.rs; scripts/gait-analyzer.js
Pose estimation 17 COCO keypoints via WiFlow architecture; dual-modal webcam+WiFi fusion demo cargo run -p wifi-densepose-sensing-server + pose-fusion demo (ADR-059); see ruview-model-training to train
Sleep monitoring Overnight monitoring, sleep-stage classification, apnea screening examples/sleep/; scripts/apnea-detector.js
Environment mapping RF fingerprinting identifies rooms, detects moved furniture, spots new objects sensing-server --build-index env; RuvSense field_model.rs, cross_room.rs
Mass Casualty Assessment (MAT) Disaster survivor detection — find people in rubble/smoke wifi-densepose-mat crate; docs/wifi-mat-user-guide.md; examples/medical/
3D point cloud (optional fusion) Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model (~22 ms, 19K+ pts/frame) scripts/mmwave_fusion_bridge.py; ADR-094 (GitHub Pages deploy)
Novel RF apps Passive radar, material classification, device fingerprinting, mincut person-counting scripts/passive-radar.js, material-classifier.js, device-fingerprint.js, mincut-person-counter.js (ADR-077/078)

Quick recipes

# Docker demo — everything, simulated CSI
docker run -p 3000:3000 ruvnet/wifi-densepose:latest    # http://localhost:3000

# Live sensing server (consumes ESP32 UDP CSI)
cd v2 && cargo run -p wifi-densepose-sensing-server

# Live RF room scan (Cognitum Seed on :5006)
node scripts/rf-scan.js --port 5006
node scripts/snn-csi-processor.js --port 5006

# Embed a trained model + build an environment index
cd v2
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env

# Python live demo
python examples/ruview_live.py

# Spectrogram / graph visualisers
node scripts/csi-spectrogram.js
node scripts/csi-graph-visualizer.js

Picking the right modality

  • Through a wall, no line of sight → presence + activity; expect ≤5 m depth (Fresnel-zone geometry).
  • Person stationary (sleeping / sitting) → vitals (breathing first, heart rate needs cleaner signal) + sleep staging.
  • Need skeletons → pose (WiFlow). Camera-free works but is modest; camera-supervised gets 92.9% PCK@20 — train it (ruview-model-training).
  • Search & rescue → MAT (docs/wifi-mat-user-guide.md).
  • "What changed in this room?" → environment mapping / RF fingerprint index.
  • Best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (ruview-advanced-sensing), optionally + Cognitum Seed.

Examples directory map

examples/environment/ · examples/medical/ · examples/sleep/ · examples/stress/ · examples/happiness-vector/ · examples/ruview_live.py — each has a README.

Reference

  • README.md — feature matrix, latency/throughput numbers
  • docs/user-guide.md, docs/wifi-mat-user-guide.md
  • ADRs: 021 (vitals), 024 (AETHER contrastive embeddings), 027 (MERIDIAN domain generalization), 041 (edge modules), 059 (live ESP32 pipeline), 077/078 (novel RF apps), 082 (pose tracker output filter), 094 (point cloud)
  • RuvSense modules: v2/crates/wifi-densepose-signal/src/ruvsense/ (14 modules)