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A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study

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...

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Autores principales: Kwasnicki, Richard M., Cross, George W. V., Geoghegan, Luke, Zhang, Zhiqiang, Reilly, Peter, Darzi, Ara, Yang, Guang Zhong, Emery, Roger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975552/
https://www.ncbi.nlm.nih.gov/pubmed/29848376
http://dx.doi.org/10.1186/s40001-018-0326-9
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author Kwasnicki, Richard M.
Cross, George W. V.
Geoghegan, Luke
Zhang, Zhiqiang
Reilly, Peter
Darzi, Ara
Yang, Guang Zhong
Emery, Roger
author_facet Kwasnicki, Richard M.
Cross, George W. V.
Geoghegan, Luke
Zhang, Zhiqiang
Reilly, Peter
Darzi, Ara
Yang, Guang Zhong
Emery, Roger
author_sort Kwasnicki, Richard M.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40001-018-0326-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-59755522018-05-31 A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study Kwasnicki, Richard M. Cross, George W. V. Geoghegan, Luke Zhang, Zhiqiang Reilly, Peter Darzi, Ara Yang, Guang Zhong Emery, Roger Eur J Med Res Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40001-018-0326-9) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-30 /pmc/articles/PMC5975552/ /pubmed/29848376 http://dx.doi.org/10.1186/s40001-018-0326-9 Text en © The Author(s) 2018 Open AccessThis 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://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Kwasnicki, Richard M.
Cross, George W. V.
Geoghegan, Luke
Zhang, Zhiqiang
Reilly, Peter
Darzi, Ara
Yang, Guang Zhong
Emery, Roger
A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title_full A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title_fullStr A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title_full_unstemmed A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title_short A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
title_sort lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975552/
https://www.ncbi.nlm.nih.gov/pubmed/29848376
http://dx.doi.org/10.1186/s40001-018-0326-9
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