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.

ruvnet Updated Sep 8, 2026

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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)

Source: ruvnet/RuView → plugins/ruview/skills/ruview-applications/SKILL.md

---name: ruview-applicationsdescription: 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.---# RuView ApplicationsWhat 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```bash# Docker demo — everything, simulated CSIdocker 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 5006node scripts/snn-csi-processor.js --port 5006# Embed a trained model + build an environment indexcd v2cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embedcargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env# Python live demopython examples/ruview_live.py# Spectrogram / graph visualisersnode scripts/csi-spectrogram.jsnode 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)---**Source:** [`ruvnet/RuView`](https://github.com/ruvnet/RuView) → `plugins/ruview/skills/ruview-applications/SKILL.md`

ruvnet/RuView commit 205d238ab0

Frequently asked questions

npx skillmds@latest add ruvnet/ruview-applications