Clin Rehabil. 2023 Jan 13:2692155221150133. doi: 10.1177/02692155221150133. Online ahead of print.
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.
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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