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Accuracy and Acceptability of Wearable Motion Tracking Smartwatches for Inpatient Monitoring

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Title: Accuracy and Acceptability of Wearable Motion Tracking Smartwatches for Inpatient Monitoring
Authors: Auepanwiriyakul, C
Waibel, S
Songa, J
Bentley, P
Faisal, AA
Item Type: Journal Article
Abstract: <jats:p>: Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted a two-stage study: First, we compared the inertial accuracy of wrist-worn IMUs, both research-grade (Xsens MTw Awinda, and Axivity AX3) and consumer-grade (Apple Watch Series 3 and 5), relative to gold-standard optical motion tracking (OptiTrack). Given the moderate to the strong performance of the consumer-grade sensors we then evaluated this sensor and surveyed the experiences and attitudes of hospital patients (N=44) and staff (N=15) following a clinical test in which patients wore smartwatches for 1.5-24 hours in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple smartwatches and Axivity AX3 (RMSE 0.17+/-0.01 g; R2 0.88+/-0.01; RMSE 0.22+/-0.01 g; R2 0.64+/-0.01; RMSE 0.42+/-0.01 g; R2 0.43+/-0.01, respectively). However, for angular velocity, the smartwatches are marginally more accurate than Xsens (RMSE 1.28+/-0.01 rad/s; R2 0.85+/-0.00; RMSE 1.37+/-0.01 rad/s; R2 0.82+/-0.01, respectively). Surveys indicated that in-patients and healthcare professionals strongly agreed that wearable motion sensors are easy to use, comfortable, unobtrusive, suitable for long term use, and do not cause anxiety or limit daily activities. Our results suggest that smartwatches achieved moderate to strong levels of accuracy compared to a gold-standard reference and are likely to be accepted as a pervasive measure of motion/behaviour within hospitals.</jats:p>
Date of Acceptance: 19-Nov-2020
URI: http://hdl.handle.net/10044/1/85868
DOI: 10.1101/2020.07.24.20160663
ISSN: 1424-8220
Publisher: MDPI AG
Journal / Book Title: Sensors
Copyright Statement: ©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RDA26
Keywords: 0502 Environmental Science and Management
0602 Ecology
Analytical Chemistry
0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
Appears in Collections:Bioengineering
Computing
Faculty of Medicine
Department of Brain Sciences



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