RuView: A WiFi sensing platform that turns radio signals into spatial intelligence.
What problem it solves
People need presence, activity, vital-sign, and environmental information in spaces where cameras or wearable devices are undesirable or impractical. The platform addresses this by capturing WiFi Channel State Information with edge sensors and converting radio disturbances into actionable sensing data.
Key features
Presence and occupancy sensing — Detect people through walls, count occupants, and track entries and exits using WiFi CSI.
Source: README
Contactless vital signs — Estimate breathing rate and heart rate while a person is sleeping or sitting, without requiring a wearable device.
Source: README
Activity and environment mapping — Recognize walking, sitting, gestures, and falls, while RF fingerprinting can identify rooms, detect moved furniture, and spot new objects.
Source: README
Camera-free pose estimation — Estimate 17 body keypoints from WiFi CSI as part of the platform's camera-free sensing capabilities.
Source: README
Edge deployment and model workflow — Run sensing on ESP32 hardware without a cloud service, and use the built-in workflow to record CSI, train models, load RVF files, and switch LoRA profiles.
Source: README, firmware, v2
Who it may suit
This project may suit developers, smart-home integrators, and wireless-sensing researchers who want to experiment with CSI-based presence, activity, and vital-sign processing.
Topics to discuss
Which CSI-capable hardware configuration provides the most practical foundation for community experiments?
How should sensing claims, model limitations, and provenance be represented in shared datasets and demonstrations?
Which local automation and smart-home integration scenarios should receive deeper community documentation?
Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.
Read the original README https://github.com/ruvnet/RuView#readme
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