Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects
Cronin, N. J., Mansoubi, M., Hannink, E., Waller, B., & Dawes, H. (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), 1087-1098. https://doi.org/10.1177/02692155221150133
Julkaistu sarjassa
Clinical RehabilitationPäivämäärä
2023Tekijänoikeudet
© 2023 the Authors
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.
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Julkaisija
SAGE PublicationsISSN Hae Julkaisufoorumista
0269-2155Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/172636046
Metadata
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Lisätietoja rahoituksesta
This work was supported by a grant from Innovate UK, The IUK project number 79429, (CRN: 11495760). National Institute for Health and Care Research Exeter Biomedical Research Centre and National Institute for Health and Care Research Exeter Clinical Research Facility support the research of Professor Helen Dawes and Dr Maedeh Mansoubi.Lisenssi
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