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.

Quality

92%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

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)

Repository: ruvnet/RuView
Path: plugins/ruview/skills/ruview-applications/SKILL.md
Commit: d9dfea2

Last updated: about 13 hours ago
First committed: 3 months ago

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.