A Wearable Sensor System with Circadian Rhythm Stability Estimation for Prototyping Biomedical Studies

Despite recent growth in the field of wearable devices, persistent collection of data with clear biomedical relevance remains elusive. The majority of products focus on short-term personal fitness metrics instead of long-term biomedical monitoring. The ideal wearable platform for researchers would include flexibility to test different biometric sensors. We present an open-source, modifiable, and user-reconfigurable wearable sensor system capable of enabling biomedical investigations not feasible with currently-available devices. Our armband device has been configured to measure skin temperature, light, and activity across days to detect internal circadian rhythms. Instability of circadian rhythms is linked to risk of many diseases, including mental illness such as depression, and has predictive power for personal affective state, yet its clinical use is slow in adoption in part because of the difficulty in acquiring relevant data. We provide evidence that such measurements are attainable at high resolution, low cost, and with minimal subject burden. Our device was tested with a variety of other sensors, and results indicate that daily circadian stability and hourly ultradian rhythms in core body temperature and hormone concentrations can be predicted from armband data. Future devices will be self-powered and perform automatic data collection to improve data continuity.