Research Article
Sleep detection using physiological signals from a wearable device
@INPROCEEDINGS{10.4108/eai.21-11-2018.2281067, author={Mahmoud Assaf and A\~{n}cha Rizzotti-Kaddouri and Magdalena Punceva}, title={Sleep detection using physiological signals from a wearable device}, proceedings={Smart City Summit Demos 2018, Guimaraes, Portugal, 21.-23. November 2018}, publisher={EAI}, proceedings_a={SMARTCITY360}, year={2019}, month={1}, keywords={sleep monitoring mobile health data collection actigraphy quantified-self wearables analytics classification}, doi={10.4108/eai.21-11-2018.2281067} }- Mahmoud Assaf
Aïcha Rizzotti-Kaddouri
Magdalena Punceva
Year: 2019
Sleep detection using physiological signals from a wearable device
SMARTCITY360
EAI
DOI: 10.4108/eai.21-11-2018.2281067
Abstract
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.
