Altmetric

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

File Description SizeFormat 
document(11).pdfPublished version1.75 MBAdobe PDFView/Open
Title: A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study
Authors: Kwasnicki, RM
Cross, GW
Geoghegan, L
Zhang, Z
Reilly, P
Darzi, A
Yang, GZ
Emery, R
Item 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.
Issue Date: 30-May-2018
Date of Acceptance: 18-May-2018
URI: http://hdl.handle.net/10044/1/60722
DOI: https://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.
Keywords: 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
11 Medical And Health Sciences
Virology
Publication Status: Published
Article Number: ARTN 28
Online Publication Date: 2018-05-30
Appears in Collections:Faculty of Engineering
Bioengineering
Division of Surgery
Computing
Faculty of Medicine



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx