Modern commercial wearable devices are widely equipped with inertial measurement units (IMU) and microphones. The motion and audio signals captured by these sensors can be used for recognizing a variety of user physical activities. Compared to motion data, audio data contains rich contextual information of human activities, but continuous audio sensing also poses extra data sampling burdens and privacy issues. Given such challenges, this paper studies a novel approach to augment IMU models for human activity recognition (HAR) with the superior acoustic knowledge of activities. Specifically, we propose a teacher-student framework to derive an IMU-based HAR model… Read more