Run RuView applications for occupancy, breathing and heart rate, activity, pose, sleep, and room changes. Choose suitable examples and startup commands for a simulated demonstration or a working ESP32 setup that captures WiFi signal measurements.
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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.
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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 | 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) |
# 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
ruview-model-training).docs/wifi-mat-user-guide.md).ruview-advanced-sensing), optionally + Cognitum Seed.examples/environment/ · examples/medical/ · examples/sleep/ · examples/stress/ · examples/happiness-vector/ · examples/ruview_live.py — each has a README.
README.md — feature matrix, latency/throughput numbersdocs/user-guide.md, docs/wifi-mat-user-guide.mdv2/crates/wifi-densepose-signal/src/ruvsense/ (14 modules)아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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