This is an open-source (GPLv3) project that uses Wi-Fi signal analysis to detect motion using CSI data, and it has already garnered almost 2,000 stars in two weeks.
Key technical details:
- The system does NOT use Machine Learning, it relies purely on Math. — Runs in real-time on a super affordable chip like the ESP32. - It integrates seamlessly with Home Assistant via MQTT.
Also, I use an ebay purchased ruckus router designed for commercial settings. Will the stronger signal and beam forming from the router provide better or worse performance, or is that mainly down to the esp32?
It cannot ignore cats or prioritize size over speed directly on the device, but ESPectre's architecture is designed to enable this kind of advanced classification externally.
It collects a rich set of pre-processed features (spatial turbulence, entropy, etc.) and transmits them via MQTT.
Any external server (like a Home Assistant add-on or a dedicated Python script) can use these features as the input for a trained ML model to perform classification (e.g., Cat vs. Human vs. Fall detection vs. Gesture detection).
Regardin Ruckus Router / Beamforming: for CSI sensing, stability is generally more important than raw power. I recommend starting by disabling beamforming or reducing the power output if you experience poor motion sensitivity, as the stability of the ESP32 receiver is often the bottleneck.