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

Recent work in Automated Dietary Monitoring (ADM) has shown promising results in eating detection by tracking jawbone movements with a proximity sensor mounted on a necklace. A significant challenge with this approach, however, is that motion artifacts introduced by natural body movements cause the necklace to move freely and the sensor to become misaligned. In this paper, we propose a different but related approach: we developed a small wireless inertial sensing platform and perform eating detection by mounting the sensor directly on the underside of the jawbone… Read more

Over the last decade, advances in mobile technologies have enabled the development of intelligent systems that attempt to recognize and model a variety of health-related human behaviors. While automated dietary monitoring based on passive sensors has been an area of increasing research activity for many years, much less attention has been given to tracking fluid intake. In this work, we apply an adaptive segmentation technique on a continuous stream of inertial data captured with a practical, off-the-shelf wrist-mounted device to detect fluid intake gestures passively… Read more