Authors:
Jan Cornelis
1
;
Elena Smets
2
and
Chris Van Hoof
3
Affiliations:
1
Imec, Leuven and Belgium
;
2
Imec, Leuven, Belgium, Electrical Engineering-ESAT, KU Leuven and Belgium
;
3
Imec, Leuven, Belgium, Electrical Engineering-ESAT, KU Leuven, Belgium, Imec, Holst Centre and The Netherlands
Keyword(s):
Sleep Detection, Wearable Devices, Actigraphy, Large Scale.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Cloud Computing
;
Devices
;
e-Health
;
Health Information Systems
;
Human-Computer Interaction
;
Physiological Computing Systems
;
Platforms and Applications
;
Practice-based Research Methods for Healthcare IT
;
Wearable Sensors and Systems
Abstract:
It has been shown that poor sleep quality and stress are major causes for mental and physical health problems in developed countries. Thanks to advancements in wearable technology, remote patient monitoring has become possible, without the need of cumbersome and expensive equipment. A method for sleep/wake detection is proposed, using chest-worn accelerometer sensors. A total of 1727 nights from 580 individuals were analysed, resulting on the identification of an average sleep time of 463 min (SD=±80 min) per day. Our algorithm was able to automatically detect 483 min (SD=±97 min) of sleep. Results show that actigraphy with an accelerometer located at the chest has potential for sleep monitoring, though further research is required for further validation, preferably using polysomnography as a benchmark.
