Our take
Activeregular commits and releases- Activity
- 90
- Community
- 50
- Issues
- 85
- Pull requests
- 46
- Releases more often than 93% of the projects we track
- Closes issues faster than 81% of the projects we track
- Merges more pull requests than 76% of the projects we track
- Slower to merge pull requests than 83% of the projects we track
RuView is an unusually broad, actively maintained WiFi-sensing project, but its sensing claims and hardware-dependent models need careful validation, and development is concentrated around one contributor.
Good fit if
- You want to prototype camera-free sensing and can provide CSI-capable hardware such as ESP32 nodes.
- Your project needs local processing or Home Assistant integration, and you are prepared to test performance in the target room.
- You want an MIT-licensed codebase with frequent commits and releases and a short recent median time to merge pull requests.
Look elsewhere if
- You need validated medical, emergency, or safety-critical measurements. The project explicitly describes these uses as prototypes, not certified systems.
- You need reliable live 17-keypoint pose estimation from a single ESP32. The README identifies that path as a weak first-cut model with a runtime stub.
- You need a project whose maintenance is broadly distributed, rather than one where most contributions come from a single person.
All health signals
| Last commit | 2026-10-26 (2 days ago) |
|---|---|
| Commits, last 90 days | 100+ |
| Releases, last 12 months | 100+ (latest v3422, 2026-10-26) |
| Contributors | 40 (top contributor: 79% of commits) |
| Issues closed, last 90 days | 14+ (typically closed in 0 days) |
| Pull requests merged, last 90 days | 52 (typically merged in 3 days) |
| Project age | 17 months |
Checked on 2026-10-29 with the GitHub API.
Overview
RuView is an edge WiFi-sensing platform that analyzes Channel State Information (CSI) to infer presence, movement, and vital signs without cameras or wearables. It is built on RuVector and Cognitum Seed, and uses low-cost ESP32 sensors to capture radio-signal changes for local processing and smart-home integrations.
The project is aimed at developers and researchers building room-level sensing, home automation, or privacy-preserving monitoring. It supports ESP32 sensing nodes, Python packages, and a Docker-based evaluation path with simulated data. Advanced sensing requires CSI-capable hardware; ordinary WiFi laptops provide only coarse RSSI-based detection.
Key Features
- Detects room presence and movement from WiFi signal changes, including through walls where signal conditions allow.
- Provides signal-processing pipelines for breathing and heart-rate estimates, with results that require validation for the target environment.
- Supports ESP32 CSI nodes and multi-node sensing, alongside research NICs and experimental hardware integrations.
- Offers Python packages for using sensing and signal-processing components in applications.
- Integrates with Home Assistant through MQTT and describes bridges for Apple Home and Matter-compatible ecosystems.
- Includes model workflows and pretrained CSI models, while clearly distinguishing benchmark results from the less mature live pose-estimation path.
- Provides a guided operator toolkit with device access, MCP support, and guarded agent workflows.
Use Cases
- Smart-home developers can use room-level presence signals to trigger lighting or HVAC automation without relying on cameras.
- Researchers can explore WiFi-based activity and vital-sign sensing using ESP32 nodes or research NICs, then validate results against their own data.
- Care and building teams can prototype contactless room monitoring, provided they independently validate measurements before operational use.
- Edge-computing developers can test local sensing pipelines and Home Assistant integrations without sending sensor data to a cloud service.
What you need
Detected in the repository
- Python >=3.9 (from pyproject.toml)
- A test suite and automated checks on GitHub Actions
License in plain words
MITpermissive
- Commercial use: yes
- Modify and redistribute: yes
- You must keep: the copyright and license notice
- Share your changes: no
A summary, not legal advice: the LICENSE file is what applies.
Getting Started
For the Python package, install with:
pip install ruview
See the README and user guide for hardware setup, Docker evaluation, and integration details.
Considerations
- Presence, vital-sign, and advanced sensing features need CSI data from compatible hardware. Docker's simulated input is useful for evaluating the software pipeline, but does not demonstrate real-world sensing performance.
- Results depend on the hardware, room, subjects, and capture conditions. The README cautions that vital-sign and pose outputs need independent validation before operational use.
- The project distinguishes published benchmark results from live device performance. In particular, the current single-ESP32 pose path is below its stated target and is not wired to produce validated pose output.
- The repository is active, with frequent recent commits and releases, but contributor activity is highly concentrated in one person. Teams adopting it should account for that maintenance dependency.
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