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

  • Marko Borazio
  • Kristof Van Laerhoven
Conference paper

DOI: 10.1007/978-3-642-25167-2_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7040)
Cite this paper as:
Borazio M., Van Laerhoven K. (2011) Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data. In: Keyson D.V. et al. (eds) Ambient Intelligence. AmI 2011. Lecture Notes in Computer Science, vol 7040. Springer, Berlin, Heidelberg

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.

Keywords

sleep behavior wearable computer long-term studies 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marko Borazio
    • 1
  • Kristof Van Laerhoven
    • 1
  1. 1.TU-DarmstadtGermany

Personalised recommendations