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  4. A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
 
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A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
File(s)
document(11).pdf (1.7 MB)
Published version
Author(s)
Kwasnicki, Richard M
Cross, George W
Geoghegan, Luke
Zhang, Zhiqiang
Reilly, Peter
more
Type
Journal Article
Abstract
Background

The prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity.
Methods

Sixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a ‘Posture’ protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an ‘Activity’ protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a ‘Proof of Concept’ model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations.
Results

The WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application.
Conclusions

The study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores.
Date Issued
2018-05-30
Date Acceptance
2018-05-18
Citation
EUROPEAN JOURNAL OF MEDICAL RESEARCH, 2018, 23 (1)
URI
http://hdl.handle.net/10044/1/60722
DOI
https://www.dx.doi.org/10.1186/s40001-018-0326-9
ISSN
0949-2321
Publisher
BIOMED CENTRAL LTD
Journal / Book Title
EUROPEAN JOURNAL OF MEDICAL RESEARCH
Volume
23
Issue
1
Copyright Statement
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (
http://creat
iveco
mmons
.org/
publi
cdoma
in/zero/1.0/
) applies to the data made available in this article, unless otherwise stated.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000433969500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Medicine, Research & Experimental
Research & Experimental Medicine
Shoulder
Pervasive
Monitoring
Posture
Sleep
Activity
Sensors
Wearables
HEART HEALTH
SHOULDER FUNCTION
ACTIGRAPHY
PAIN
ASSOCIATION
PREVALENCE
SYMPTOMS
CHILDREN
ACCURATE
APNEA
Publication Status
Published
Article Number
ARTN 28
Date Publish Online
2018-05-30
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