# RuView: Turn WiFi CSI into Spatial Intelligence This repository profile is provided by osrepos.com, an open source repository discovery platform. Source: osrepos.com Repository profile: https://osrepos.com/repo/ruvnet-ruview Generated for open source discovery and AI-assisted research. RuView uses WiFi Channel State Information from ESP32 sensors to detect presence, movement, and vital signs without cameras or wearables. Built on RuVector and Cognitum Seed, it targets privacy-conscious edge sensing and smart-home automation. GitHub: https://github.com/ruvnet/RuView OSRepos URL: https://osrepos.com/repo/ruvnet-ruview ## Summary RuView uses WiFi Channel State Information from ESP32 sensors to detect presence, movement, and vital signs without cameras or wearables. Built on RuVector and Cognitum Seed, it targets privacy-conscious edge sensing and smart-home automation. ## Topics - rust - ai - automation - privacy - wifi-sensing - vital-sign-monitoring - channel-state-information ## Repository Information Last analyzed by OSRepos: Thu Oct 29 2026 00:34:22 GMT+0000 (Western European Standard Time) Detail views: 2 GitHub clicks: 0 ## Safety Notice OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything. ## Content ## 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. ## Our Take 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. ## Project Health | Signal | Value | |---|---| | Status | **Active**: regular commits and releases | | 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. ## Project Facts - Language: Rust - License: MIT - Stars: 101k - Forks: 13.2k - Topics: awesome, claude, densepose, esp32, firmware, home-assistant, home-automation, iot - Website: https://Cognitum.One/RuView - Archived: no ## What You Need Detected in the repository: - Python >=3.9 (from pyproject.toml) - A test suite and automated checks on GitHub Actions ## Getting Started For the Python package, install with: ```bash pip install ruview ``` See the [README](https://github.com/ruvnet/RuView) and [user guide](https://github.com/ruvnet/RuView/blob/main/docs/user-guide.md) for hardware setup, Docker evaluation, and integration details. ## License in Plain Words **MIT** (permissive). - 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. ## 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.