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Barsocchi P., Bianchini M., Crivello A., La Rosa D., Palumbo F., Scarselli F. An unobtrusive sleep monitoring system for the human sleep behaviour understanding. In: CogInfoCom 2016 - 7th IEEE International Conference on Cognitive Infocommunications (Wroclaw, Poland, 16-18 October 2016). Proceedings, pp. 91 - 96. IEEE, 2016.
 
 
Abstract
(English)
Sleep plays a vital role in good health and well-being throughout our life. Getting enough quality sleep at the right times can help protect mental and physical health, quality of life, and safety. Emerging wearable devices allow people to measure and keep track of sleep duration, patterns, and quality. Often, these approaches are intrusive and change the user's daily sleep habits. In this paper, we present an unobtrusive approach for the detection of sleep stages and positions. The proposed system is able to overcome the weakness of classic actigraphy-based systems, since it is easy to deploy and it is based on inexpensive technology. With respect to the actigraphy-based systems, the proposed system is able to detect the bed posture, that is crucial to support pressure ulcer prevention (i.e. bedsores). Results from our algorithm look promising and show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach.
URL: http://ieeexplore.ieee.org/document/7804531/
DOI: 10.1109/CogInfoCom.2016.7804531
Subject Sleeping positions classification
Sleep behaviour recognition
Sleep monitoring
Pattern Recognition
I.5 PATTERN RECOGNITION


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