Training a machine-learned module for radar-based gesture detection in an ambient compute environment

Abstract

Techniques and apparatuses are described that train machine-learned modules to perform radar-based gesture detection in an ambient compute environment. Compared to other smart devices that rely on a physical user interface, a smart device () with a radar system () can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system () can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. The radar system () uses an ambient-computing machine-learned module () to quickly recognize gestures performed by a user up to at least two meters away. The ambient-computing machine-learned module () is trained, at least in part, using a two-phase evaluation process, which includes a segmented classification task and an unsegmented recognition task.