Abstract
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.
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Assaf, M., Rizzotti-Kaddouri, A., Punceva, M. (2020). Sleep Detection Using Physiological Signals from a Wearable Device. In: Inácio, P., Duarte, A., Fazendeiro, P., Pombo, N. (eds) 5th EAI International Conference on IoT Technologies for HealthCare. HealthyIoT 2018. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-30335-8_3
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DOI: https://doi.org/10.1007/978-3-030-30335-8_3
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