Sleep detection using physiological signals from a wearable device

Assaf, Mahmoud (HES-SO Master, HES-SO // University of Applied Sciences Western Switzerland) ; Rizzotii, Aïcha (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland) ; Punceva, Magdalena (School of Engineering – HE-Arc Ingénierie, HES-SO // University of Applied Sciences Western Switzerland)

Internet of things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytics. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of am- bulatory 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 consists 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.


Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HE-Arc Ingénierie
Institute:
Aucun institut
Publisher:
Guimaraes, Portugal, 21-23 November 2018
Date:
2018-11
Guimaraes, Portugal
21-23 November 2018
Pagination:
15 p.
Published in:
Proceedings of Smart City Demos 2018, 21-23 November 2018, Guimaraes, Portugal
DOI:
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-11-26, last modified 2019-11-29

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