Tool Open source
wifi-sensing-kit is an open-source local Wi-Fi sensing kit that uses three ESP32-S3 boards to recognize activities such as sitting, squatting, walking, jumping, or an empty room from changes in Wi-Fi channel state information (CSI). During training, a webcam uses a pose model to label movements; the resulting movement model can then classify activity from Wi-Fi while the camera is covered. The kit provides dashboards for live CSI signals, a room map showing sensing links between boards and the router, and movement-model training, testing, and evaluation. It is built on RuView's open-source CSI firmware, with a patch that gives each board a distinct light color and lets boards measure one another as well as the router. Processing is local: webcam video stays in the browser and only movement labels are sent to the local server. The system must be retrained when the person, room, or board positions change, and it does not produce a skeleton or exact position. The repository includes firmware build and flashing scripts, a local Python server, configuration files, and evaluation tools; it requires three ESP32-S3 boards with at least 8 MB of flash, a 2.4 GHz Wi-Fi network, and a macOS or Linux computer for setup. The kit's code is MIT-licensed, while its bundled firmware patch, pose model, and fonts retain their stated upstream licenses.
1 use taken from transcripts — each links to the moment in the video.
Teaches ESP32 boards to recognize motion from Wi-Fi signal changes; a webcam labels what you do during training and afterwards Wi-Fi does the recognition with the webcam covered, all processed locally with dashboards showing signals and predicted activity, though moving the person, room, or board requires retraining.
1 in the library.