{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:44:02Z","timestamp":1785602642107,"version":"3.56.0"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute for Health Research Imperial College London Biomedical Research Centre."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"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), and optical motion tracking (OptiTrack). Given the moderate to 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\u201324 h in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple Series 5 and 3 smartwatches and Axivity AX3 (RMSE 1.66 \u00b1 0.12 m\u00b7s\u22122; R2 0.78 \u00b1 0.02; RMSE 2.29 \u00b1 0.09 m\u00b7s\u22122; R2 0.56 \u00b1 0.01; RMSE 2.14 \u00b1 0.09 m\u00b7s\u22122; R2 0.49 \u00b1 0.02; RMSE 4.12 \u00b1 0.18 m\u00b7s\u22122; R2 0.34 \u00b1 0.01 respectively). For angular velocity, Series 5 and 3 smartwatches achieved similar performances against Xsens with RMSE 0.22 \u00b1 0.02 rad\u00b7s\u22121; R2 0.99 \u00b1 0.00; and RMSE 0.18 \u00b1 0.01 rad\u00b7s\u22121; R2 1.00\u00b1 SE 0.00, 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 consumer smartwatches achieved moderate to strong levels of accuracy compared to laboratory gold-standard and are acceptable for pervasive monitoring of motion\/behaviour within hospital settings.<\/jats:p>","DOI":"10.3390\/s20247313","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"7313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8720-5188","authenticated-orcid":false,"given":"Chaiyawan","family":"Auepanwiriyakul","sequence":"first","affiliation":[{"name":"Brain &amp; Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK"},{"name":"Behaviour Analytics Lab, Data Science Institute, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5053-7365","authenticated-orcid":false,"given":"Sigourney","family":"Waibel","sequence":"additional","affiliation":[{"name":"Brain &amp; Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK"},{"name":"Department of Brain Sciences, Imperial College London, London W12 0NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Songa","sequence":"additional","affiliation":[{"name":"Department of Brain Sciences, Imperial College London, London W12 0NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-7010","authenticated-orcid":false,"given":"Paul","family":"Bentley","sequence":"additional","affiliation":[{"name":"Department of Brain Sciences, Imperial College London, London W12 0NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0813-7207","authenticated-orcid":false,"given":"A. Aldo","family":"Faisal","sequence":"additional","affiliation":[{"name":"Brain &amp; Behaviour Lab, Department of Computing, Imperial College London, London SW7 2AZ, UK"},{"name":"Behaviour Analytics Lab, Data Science Institute, London SW7 2AZ, UK"},{"name":"UKRI CDT in AI for Healthcare, Imperial College London, London SW7 2AZ, UK"},{"name":"MRC London Institute of Medical Sciences, London W12 0NN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"ref_1","unstructured":"Salter, K., Campbell, N., Richardson, M., Mehta, S., Jutai, J., Zettler, L., Moses, M.B.A., McClure, A., Mays, R., and Foley, N. (2019, June 22). EBRSR [Evidence-Based Review of Stroke Rehabilitation] 20 Outcome Measures in Stroke Rehabilitation. 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