Sleep Monitoring Data Validation using a Linear Discriminant Classifier

Jose Perez-Macias Martin, Michael Pavel, Ilkka Korhonen, Holly Jimison

    Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu


    Sleep quality has an important impact in
    overall health. There are several new commercially
    available devices for sleep tracking. Although they have
    been tested in sleep laboratories, for some users,
    estimates are reliable whereas for others are very
    different. In this paper we present a framework to
    predict the error range of the total sleep time. We
    studied 23 subjects’ sleep patterns using Fitbit One and
    Beddit Pro devices. We used Beddit Pro as our
    reference devices to verify Fitbit’s reliability. Previous
    analyses found that for some users the agreement
    between systems was high, whereas for others was low.
    We used a linear discriminant to predict this behavior
    in in the Fitbit data. 72.7% accuracy was achieved. Our
    results suggest that reliability of sleep estimates could
    be estimated without the use of a second device.
    Otsikko7th annual Canadian Student Conference on Biomedical Computing and Engineering and IEEE EMBS International Student Conference (ISC)
    TilaJulkaistu - 25 kesäk. 2014
    OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
    TapahtumaIEEE EMBS International Student Conference (ISC'14 Oshawa) - University of Ontario Institute of Technology (, Oshawa, Kanada
    Kesto: 24 kesäk. 201426 kesäk. 2014


    ConferenceIEEE EMBS International Student Conference (ISC'14 Oshawa)
    LyhennettäCSCBCE / ISC


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