Toward Detecting Student-Athletes' Condition Using Passive Mobile and Wearable Sensing

Student-athletes (SAs) face stress from balancing athletic and academic demands; therefore, monitoring their physical and mental stress is crucial to ensure better well-being. Questionnaires and dedicated measurement equipment have been used to assess the mental and physical status of SAs. However, these methods are not scalable and are difficult to use continuously. In this paper, we propose a method for monitoring the physical and mental state of SAs using passive mobile and wearable sensing technology to minimize the burden associated with monitoring. First, we developed a platform for collecting daily, training, and resetting behavior data from smartphones and wearable devices. Second, as a preliminary study, we collected the behavior and Stress and Recovery States Scale (SRSS) data for four weeks from 19 SAs and analyzed the collected data to understand their unique behavior patterns and living environments. The results demonstrate that wearable devices and smartphones can automatically collect data on "exercise intensity during competitions" and "lifestyle patterns," specifically tailored to the mental and physical states of SAs. Furthermore, this preliminary research establishes a foundation for future efforts using machine learning to predict the physical and mental states of SAs.