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

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 (r(2) values: 0.360-0.768). There were no significant mean differences between methods for shoulder flexion (left: 2 +/- 14 degrees (mean +/- standard deviation), t = 0.99, p < 0.33; right: 3 +/- 15 degrees, 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.

Automated summary

Advances in computer vision enable biomechanical measures of body function and rehabilitation programs to be performed anywhere. This study evaluated the accuracy and concurrent validity of a computer vision system for estimating clinically relevant biomechanical measures. The results showed a significant correlation between computer vision estimates and clinician measures.