Activity Recognition and Stress Detection via Wristband

Advancement of micro-electromechanical systems enables easy daily activity and physiological data collection with a smart wristband and smartphone. Making use of those signals in various intelligent algorithm can contribute much to trending m-health applications. The ability of continuously monitoring physical activities and stress level can help users to better track their health condition. In this study, we propose to recognize different physical activities and detect long lasting stress level based on the 3-axis acceleration signals and physiological signals. We are able to achieve accuracy of around 97% for physical activities recognition and more than 80% for stress detection. We also discover that physiological signals alone cannot distinguish well between the high intensity activities and the stress condition.