Monitoring Physical Activity and Mental Stress Using Wrist-Worn Device and a Smartphone

Abstract

The paper presents a smartphone application for monitoring physical activity and mental stress. The application utilizes sensor data from a wristband and/or a smartphone, which can be worn in various pockets or in a bag in any orientation. The presence and location of the devices are used as contexts for the selection of appropriate machine-learning models for activity recognition and the estimation of human energy expenditure. The stress-monitoring method uses two machine-learning models, the first one relying solely on physiological sensor data and the second one incorporating the output of the activity monitoring and other context information. The evaluation showed that we recognize a wide range of atomic activities with the accuracy of 87%, and that we outperform the state-of-the art consumer devices in the estimation of energy expenditure. In stress monitoring we achieved the accuracy of 92% in a real-life setting.