RuView is an open-source project that turns ordinary WiFi into a sensor for the people in a room. WiFi chips measure how a radio wave arrives after bouncing around a space, a reading called Channel State Information. A breathing chest, a beating heart, or a walking person each shift those reflections, and RuView learns to read them. From a $9 ESP32 board it detects presence, estimates breathing (6 to 30 breaths a minute) and heart rate (40 to 120 beats a minute), counts people, flags falls, and tracks 17 body joints, with no camera and no wearable, even in the dark. Its pose model scores 82.69% on MM-Fi, a public WiFi-sensing benchmark, ahead of earlier results of 72.25% and 68.41%.
The appeal is cost and privacy. A camera in a bedroom or a care home raises obvious problems; a $9 chip that senses only radio reflections records no pixels, and all processing stays on the device. For elderly care that means fall alerts and overnight breathing and heart-rate checks with no wearable and no lens pointed at a bed. The pose model runs in 8.4 milliseconds on a Raspberry Pi 5, cheap enough for every room.
What stands out is the honesty about limits. The team corrected its pose score down from an inflated 91.86% to 82.69% before publishing, then showed that in a room the model has never seen, accuracy falls to 17.5%. The radio physics works; making it generalize from one room to any is the unsolved part.
Read More: FreeMoCap reaches the same kind of 3D body pose from ordinary webcams, where RuView uses no camera at all.
Sources:
- RuView on GitHub
- WiFi-DensePose pose model (Hugging Face)
- WiFi-DensePose sensing model (Hugging Face)
- ruview on PyPI
- RuView Observatory live demo
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Citation
@misc{kabui2026,
author = {{Kabui, Charles}},
title = {RuView: {Sensing} {People} {With} {WiFi,} {Not} {Cameras}},
date = {2026-06-12},
url = {https://toknow.ai/posts/ruview-wifi-sensing-breathing-pose-no-camera/},
langid = {en-GB}
}