Sleep Detection Using Physiological Signals from a Wearable Device

Internet of Things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytic. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of ambulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consist of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality.