Smart City Summit Demos 2018, Guimaraes, Portugal, 21.-23. November 2018

Research Article

Sleep detection using physiological signals from a wearable device

Download146 downloads
  • @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
Mahmoud Assaf1,*, Aïcha Rizzotti-Kaddouri2, Magdalena Punceva3
  • 1: HES-SO
  • 2: Haute Ecole Arc /HES-SO
  • 3: Haute Ecole Arc/ HES-SO
*Contact email: mahmoud.assaf@master.hes-so.ch

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.