- Category
- AI Tools
- Rank
- No. 158Tools index
- Pricing
- Open Source
- Platform
- cli · web · desktop
- Type
- TOOL
- Builder
- @ruvnet
- GitHub
- 92.7k stars
- Latest release
- v2567
- Date
About
RuView transforms commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection without cameras or wearables. It uses Channel State Information (CSI) from WiFi to detect breathing, heart rate, and body position through walls using edge AI on inexpensive ESP32 hardware.
What it does
RuView is a broad local sensing stack, not merely a model. Sensor nodes collect radio measurements, processing pipelines extract motion and physiological patterns, and servers expose results through dashboards, streaming endpoints, automation bridges, and programmable clients. Simulated data supports evaluation before hardware deployment.
Why it's ranked here
The project earns attention for unusually broad delivery surfaces and candid documentation of weak spots. It supplies firmware, signal processing, APIs, visualization, reproducible proof tooling, and home automation integration. However, the bundled live pose model is explicitly described as a first cut with 3.0% PCK@20, while its runtime confidence remains a stub.
What's good
Installation paths cover Docker, Rust crates, Python wheels, WebAssembly, and a guided installer. Deterministic reference signals let engineers replay the processing chain without sensors. REST, WebSocket, MQTT, Matter, HomeKit, and OpenTelemetry support make the output usable beyond a demo. The troubleshooting guide documents concrete field failures and workarounds.
Tradeoffs
Capability varies sharply by input and model. Ordinary laptop signal strength supports coarse presence and motion, not pose or reliable vital signs. Full sensing needs compatible CSI hardware and environmental calibration. Person counting can overcount, current vital estimates use a simpler pipeline than the available advanced implementation, and several ambitious world-model claims still lack real-data validation.
How to use it well
Use RuView for experimental RF sensing, edge research, smart-home prototypes, or building a locally operated telemetry pipeline. Start with deterministic verification and simulation, then add ESP32 nodes and calibrate in the target room. Treat pose, counting, sleep, and health-related outputs as measurements requiring independent validation, not as substitutes for cameras, clinical devices, or safety-certified monitoring.
Technical notes+
README.md positions the Rust sensing server and ESP32 mesh as the current core while acknowledging that the committed pose_v1 model has PCK@20 = 3.0% and a confidence=0 runtime stub. Makefile exposes guided install profiles, Rust and WASM builds, workspace tests, benchmarks, API startup, visualization, Docker, and deterministic verification. pyproject.toml defines the legacy Python package, Python 3.9+ support, CLI entry points, FastAPI and ML dependencies, and strict pytest coverage configuration. docs/user-guide.md documents Docker, independently published Rust crates, PyO3 wheels, REST and WebSocket interfaces, simulation, RSSI modes, and ESP32 CSI. docs/build-guide.md explains the archived Python pipeline and Rust workspace. docs/observability.md gates OTLP export behind both the otel Cargo feature and OTEL_EXPORTER_OTLP_ENDPOINT. docs/TROUBLESHOOTING.md records unresolved or partial issues including person-count overestimation, vital-sign jitter, and Windows Docker UDP forwarding.
Observed
- License
- MIT
- Languages
- Rust and Python, with C firmware and browser JavaScript components
- Install surfaces
- Docker image, guided installer, Python wheels, Rust crates, and source builds
- Interfaces
- CLI, Python library, Rust libraries, REST API, WebSocket streams, MQTT, Matter, and MCP
- Platform support
- Windows, macOS, and Linux are documented; Docker images support amd64 and arm64
- Hardware support
- ESP32-S3 CSI nodes, Intel 5300, Atheros hardware, and RSSI-only laptop sensing
- Browser support
- WebAssembly bindings and a Three.js visualization are included
- Verification structure
- Deterministic signal replay uses a published reference signal and SHA-256 output comparison
Read from README.md, Makefile, pyproject.toml, requirements.txt, docs/user-guide.md, docs/build-guide.md, docs/observability.md, docs/readme-details.md, docs/TROUBLESHOOTING.md, docs/WITNESS-LOG-028.md, docs/WITNESS-LOG-110.md.
What it can do
Estimate human body pose through walls
WiFi Channel State Information (CSI) signals → Real-time human pose data
Monitor breathing rate without physical contact
WiFi signal variations → Breathing rate measurements
Detect heart rate through WiFi signals
WiFi Channel State Information (CSI) → Heart rate measurements
Detect human presence in rooms
WiFi signal patterns → Presence detection status
Monitor body position changes
WiFi Channel State Information (CSI) → Body position data
Process WiFi signals for vital sign extraction
Raw WiFi Channel State Information → Processed vital sign metrics
Tags
Tech Stack
Media
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.