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

Automatically recognizing a broad spectrum of human activities is key to realizing many compelling applications in health, personal assistance, human-computer interaction and smart environments. However, in real-world settings, approaches to human action perception have been largely constrained to detecting mobility states, e.g., walking, running, standing. In this work, we explore the use of inertial-acoustic sensing provided by off-the-shelf commodity smartwatches for detecting activities of daily living (ADLs). We conduct a semi-naturalistic study with a diverse set of 15 participants in their own homes and show that acoustic and inertial sensor data can be combined to recognize 23 activities… Read more