TinyML-Based Real-Time Doorway Activity Recognition with a Time-of-Flight Sensor

The present paper sets out a fully embedded system for real-time classification of human motion events at a doorway using a Time-of-Flight sensor and a Tiny Machine Learning model deployed on an Arduino Nano 33 IoT. The system is capable of distinguishing between three distinct behaviors: entering, exiting, and approaching–leaving. This is achieved by analyzing sequences of distance measurements using a quantized 1D convolutional neural network. A dataset was collected from 15 participants, and the model was evaluated using both 70–30 and leave-one-subject-out validation protocols. The quantized model demonstrated an accuracy of over 95% in subject-aware testing and above 91% in subject-independent scenarios. When deployed on the microcontroller, the system demonstrated an inference time of 275 ms, an average power consumption of 16 mW, and 26 KB of SRAM usage. These results confirm the feasibility of accurate, real-time human motion classification in a compact, battery-friendly architecture suitable for smart environments and embedded monitoring.