Recognizing Sleep Stages with Wearable Sensors in Everyday Settings

Ulrich Reimer, Sandro Emmenegger, Edith Maier, Zhongxing Zhang, Ramin Khatami

Abstract

The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addresses two sub-tasks: detecting a user’s daytime behaviour and recognising sleep stages in an everyday setting. The focus of the paper is on the second task. Various sensor devices from the consumer market were used in addition to the usual PSG sensors in a sleep lab. An expert assigned a sleep stage for every 30 seconds. Subsequently, a sleep stage classifier was learned from the resulting sensor data streams segmented into labelled sleep stages of 30 seconds each. Apart from handcrafted features we also experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are in the range of 90 to 91% for Wake, REM and N3, while the best recognition rate for N2 is 83%. The classification results for N1 turned out to be much worse, N1 being mostly confused with N2.

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Paper Citation


in Harvard Style

Reimer U., Emmenegger S., Maier E., Zhang Z. and Khatami R. (2017). Recognizing Sleep Stages with Wearable Sensors in Everyday Settings . In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, ISBN 978-989-758-251-6, pages 172-179. DOI: 10.5220/0006346001720179


in Bibtex Style

@conference{ict4awe17,
author={Ulrich Reimer and Sandro Emmenegger and Edith Maier and Zhongxing Zhang and Ramin Khatami},
title={Recognizing Sleep Stages with Wearable Sensors in Everyday Settings},
booktitle={Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,},
year={2017},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006346001720179},
isbn={978-989-758-251-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,
TI - Recognizing Sleep Stages with Wearable Sensors in Everyday Settings
SN - 978-989-758-251-6
AU - Reimer U.
AU - Emmenegger S.
AU - Maier E.
AU - Zhang Z.
AU - Khatami R.
PY - 2017
SP - 172
EP - 179
DO - 10.5220/0006346001720179