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Understanding Human Sleep Behaviour by Machine Learning

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Cognitive Infocommunications, Theory and Applications

Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 13))

Abstract

Long-term sleep quality assessment is essential to diagnose sleep disorders and to continuously monitor the health status. However, traditional polysomnography techniques are not suitable for long-term monitoring, whereas, methods able to continuously monitor the sleep pattern in an unobtrusive way are needed. In this paper, we present a general purpose sleep monitoring system that can be used for the pressure ulcer risk assessment, to monitor bed exits, and to observe the influence of medication on the sleep behavior. Moreover, we compare several supervised learning algorithms in order to determine the most suitable in this context. Experimental results obtained by comparing the selected supervised algorithms show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach.

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Acknowledgements

This work is supported by the INTESA “Servizi ICT integrati per il benessere di soggetti fragili” project, under the APQ MIUR MISE Regione Toscana (DGRT 758 del 16/09/2013) FAR–FAR 2014 Program.

Author information

Authors and Affiliations

  1. Information Science and Technologies Institute, National Research Council of Italy, Pisa, Italy

    Antonino Crivello, Filippo Palumbo, Paolo Barsocchi & Davide La Rosa

  2. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Antonino Crivello, Franco Scarselli & Monica Bianchini

Authors
  1. Antonino Crivello
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  2. Filippo Palumbo
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  3. Paolo Barsocchi
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  4. Davide La Rosa
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  5. Franco Scarselli
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  6. Monica Bianchini
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Corresponding author

Correspondence to Antonino Crivello .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wrocław University of Science and Technology, Wrocław, Poland

    Ryszard Klempous

  2. Faculty of Electronics, Wrocław University of Science and Technology, Wrocław, Poland

    Jan Nikodem

  3. Széchenyi István University, Győr, Hungary

    Péter Zoltán Baranyi

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Crivello, A., Palumbo, F., Barsocchi, P., La Rosa, D., Scarselli, F., Bianchini, M. (2019). Understanding Human Sleep Behaviour by Machine Learning. In: Klempous, R., Nikodem, J., Baranyi, P. (eds) Cognitive Infocommunications, Theory and Applications. Topics in Intelligent Engineering and Informatics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-95996-2_11

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