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)