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

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 rel...

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Bibliographic Details
Published in:Clinical rehabilitation Vol. 37; no. 8; p. 1087
Main Authors: Cronin, Neil J, Mansoubi, Maedeh, Hannink, Erin, Waller, Benjamin, Dawes, Helen
Format: Journal Article
Language:English
Published: England 01.08.2023
Subjects:
Artificial Intelligence
Biomechanical Phenomena
Cross-Sectional Studies
Healthy Volunteers
Humans
Spondylarthropathies
Spondylitis, Ankylosing - drug therapy
remote monitoring
Artificial intelligence
physiotherapy
telerehabilitation
computer vision
clinical test
ISSN:1477-0873, 1477-0873
Online Access:Get more information
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Summary: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. Cross-sectional study. Laboratory. Thirty-one healthy participants and 31 patients with axial spondyloarthropathy. 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. Joint angles and inter-limb distances. Clinician measures were compared with computer vision estimates. For all tests, clinician and computer vision estimates were correlated ( values: 0.360-0.768). There were no significant mean differences between methods for shoulder flexion (left: 2 ± 14° (mean ± standard deviation),  = 0.99, < 0.33; right: 3 ± 15°,  = 1.57, < 0.12), side flexion (left: - 0.5 ± 3.1 cm,  = -1.34,  = 0.19; right: 0.5 ± 3.4 cm,  = 1.05,  = 0.30) and lumbar flexion ( - 1.1 ± 8.2 cm,  = -1.05,  = 0.30). For all other movements, significant differences were observed, but could be corrected using a systematic offset. 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.
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ISSN:1477-0873
1477-0873
DOI:10.1177/02692155221150133