TrustTiny-HAR: Selective, Open-Set, and Calibrated Activity Recognition on Microcontrollers

TrustTiny-HAR is a system that enables microcontrollers (small, low-power computers) to recognize human activities while being honest about uncertainty, rather than confidently guessing when faced with unfamiliar situations. The system combines a compact machine learning model with special techniques to detect when it lacks sufficient evidence to make a prediction and can optionally send only a small data summary (a 32-64 dimensional feature sketch) to a more powerful device instead of raw sensor data. When tested on real activity datasets with various challenging conditions like unseen activity types and sensor placement shifts, it maintained strong recognition accuracy while improving calibration (confidence reliability) and reliably rejecting out-of-distribution scenarios.