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

Borazio, Marko ; Laerhoven, Kristof Van (2011):
Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data.
LNCS 7, pp. 151-156, Amsterdam, Springer Verlag, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: Borazio, Marko ; Laerhoven, Kristof Van
Title: Predicting Sleeping Behaviors in Long-Term Studies with Wrist-Worn Sensor Data
Language: English
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.

Volume: LNCS 7
Place of Publication: Amsterdam
Publisher: Springer Verlag
Divisions: 20 Department of Computer Science
Date Deposited: 17 Jan 2012 09:56
Official URL: http://www.springerlink.com/content/h955508761143442/
Additional Information:

Embedded Sensing Systems

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