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Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data

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Ambient Intelligence (AmI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7040))

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Abstract

This paper conducts a preliminary study in which sleeping behavior is predicted using long-term activity data from a wearable sensor. For this purpose, two scenarios are scrutinized: The first predicts sleeping behavior using a day-of-the-week model. In a second scenario typical sleep patterns for either working or weekend days are modeled. In a continuous experiment over 141 days (6 months), sleeping behavior is characterized by four main features: the amount of motion detected by the sensor during sleep, the duration of sleep, and the falling asleep and waking up times. Prediction of these values can be used in behavioral sleep analysis and beyond, as a component in healthcare systems.

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Author information

Authors and Affiliations

  1. TU-Darmstadt, Germany

    Marko Borazio & Kristof Van Laerhoven

Authors
  1. Marko Borazio
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  2. Kristof Van Laerhoven
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Editor information

Editors and Affiliations

  1. Delft University of Technology, Landbergstraat 15, 2628CE, Delft, The Netherlands

    David V. Keyson

  2. College of Information Studies, University of Maryland, College Park, 2117E Hornbake Hall, 20742-4345, Marylandm, USA

    Mary Lou Maher

  3. Smart Future Initiative, Konrad-Zuse-Str. 43, 60438, Frankfurt, Germany

    Norbert Streitz

  4. Keio University, Japan and National University of Singapore, Heng Miu Keng Terrace 02-01-01A, 119613, Singapore

    Adrian Cheok

  5. School of Computing and Mathematics, University of Ulster, Shore Road, BT37 0QB, Newtownabbey, Co. Antrim, UK

    Juan Carlos Augusto

  6. Fraunhofer-Institut für Graphische Datenverarbeitung IGD, Fraunhoferstrasse 5, 64283, Darmstadt, Germany

    Reiner Wichert

  7. Informatics Institute, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands

    Gwenn Englebienne

  8. Ambient Intelligence Research Lab., Stanford University and Ghent University, 350 Serra Mall, David Packard #318, 94305-9515, Stanford, CA, USA

    Hamid Aghajan

  9. Informatics Institute, University of Amsterdam and Amsterdam University of Applied Sciences, Science Park 904, 1098XH, Amsterdam, The Netherlands

    Ben J. A. Kröse

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© 2011 Springer-Verlag Berlin Heidelberg

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Borazio, M., Van Laerhoven, K. (2011). Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data. In: Keyson, D.V., et al. Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25167-2_18

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  • DOI: https://doi.org/10.1007/978-3-642-25167-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25166-5

  • Online ISBN: 978-3-642-25167-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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