Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects

Cronin, Neil ORCID: 0000-0002-5332-1188, Mansoubi, Maedeh, Hannink, Erin, Waller, Benjamin and Dawes, Helen (2023) Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects. Clinical Rehabilitation, 37 (8). pp. 1087-1098. doi:10.1177/02692155221150133

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Text 12272 Cronin, Mansoubi, Hannink, Waller, Dawes (2023) Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview

Abstract

Objective: Advances in computer vision make it possible to combine low-cost cameras with algorithms, enabling biomechanical measures of body function and rehabilitation programs to be performed anywhere. We evaluated a computer vision system's accuracy and concurrent validity for estimating clinically relevant biomechanical measures. Design: Cross-sectional study. Setting: Laboratory. Participants: Thirty-one healthy participants and 31 patients with axial spondyloarthropathy. Intervention: A series of clinical functional tests (including the gold standard Bath Ankylosing Spondylitis Metrology Index tests). Each test was performed twice: the first performance was recorded with a camera, and a computer vision algorithm was used to estimate variables. During the second performance, a clinician measured the same variables manually. Main measures: Joint angles and inter-limb distances. Clinician measures were compared with computer vision estimates. Results: For all tests, clinician and computer vision estimates were correlated (r2 values: 0.360–0.768). There were no significant mean differences between methods for shoulder flexion (left: 2 ± 14° (mean ± standard deviation), t = 0.99, p < 0.33; right: 3 ± 15°, t = 1.57, p < 0.12), side flexion (left: − 0.5 ± 3.1 cm, t = −1.34, p = 0.19; right: 0.5 ± 3.4 cm, t = 1.05, p = 0.30) and lumbar flexion ( − 1.1 ± 8.2 cm, t = −1.05, p = 0.30). For all other movements, significant differences were observed, but could be corrected using a systematic offset. Conclusion: We present a computer vision approach that estimates distances and angles from clinical movements recorded with a phone or webcam. In the future, this approach could be used to monitor functional capacity and support physical therapy management remotely.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Artificial intelligence; Physiotherapy; Clinical test; Telerehabilitation; Remote monitoring; Computer vision
Subjects:	Q Science > QM Human anatomy R Medicine > RB Pathology
Divisions:	Schools and Research Institutes > School of Education and Science
Research Priority Areas:	Health, Life Sciences, Sport and Wellbeing
Depositing User:	Anna Kerr
Date Deposited:	19 Jan 2023 09:36
Last Modified:	31 Aug 2023 09:06
URI:	https://eprints.glos.ac.uk/id/eprint/12272

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