ruview-applications
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
More from ruvnet/ruview
browser
Web browser automation with AI-optimized snapshots for claude-flow agents
24skill builder
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.
22v3 deep integration
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
21agentdb memory patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
20v3 performance optimization
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
20swarm orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
20