RuView Advanced Sensing Application Suite

v20260719

ruview-applications

A comprehensive suite for running advanced sensing applications using RuView hardware. It supports multi-modal data fusion from CSI, Wi-Fi, and mmWave sensors to perform critical tasks such as occupancy detection, vital sign monitoring (heart rate, breathing), pose estimation, environment mapping, and mass casualty assessment. Ideal for prototyping sophisticated IoT and smart building solutions.

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Overview

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)

Info

Category Artificial Intelligence

Name ruview-applications

Version v20260719

Size 4.48KB

Source ruvnet/RuView

Updated At 2026-07-21