ArticleSensors (Basel, Switzerland)2025
Reliability and Validity of the Single-Camera Markerless Motion Capture System for Measuring Shoulder Range of Motion in Healthy Individuals and Patients with Adhesive Capsulitis: A Single-Center Study.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Assessing shoulder joint range of motion (ROM) is essential for diagnosing musculoskeletal disorders and optimizing treatments. This single-center pilot study evaluated the reliability and validity of iBalance, a single-camera markerless motion capture system, for measuring shoulder ROM. Forty participants (30 healthy individuals and 10 patients with adhesive capsulitis) underwent measurements of seven shoulder joint movements. Each movement was assessed three times by two raters using both iBalance and a goniometer, with measurements repeated after 1 week. The iBalance demonstrated excellent inter- and intra-rater reliability for flexion (ICC = 0.93 [0.91-0.95], 0.91 [0.88-0.94]), abduction (ICC = 0.97 [0.95-0.98], 0.93 [0.91-0.95]), and passive abduction (ICC = 0.97 [0.96-0.98], 0.98 [0.97-0.98]). The system also showed strong validity compared to the goniometer for flexion (ICC = 0.85 [0.68-0.92]), abduction (ICC = 0.95 [0.94-0.96]), and passive abduction (ICC = 0.97 [0.96-0.98]). Bland-Altman plots showed high consistency between the two devices for flexion, abduction, and passive abduction, with most data points falling within the limits of agreement. Patients with adhesive capsulitis exhibited greater variability than healthy individuals. No adverse events were reported, supporting the safety of the system. This study highlights the potential of a single-camera markerless motion capture system for diagnosing and treating shoulder joint disorders. The iBalance showed clinical applicability for measuring flexion, abduction, and passive abduction. Future enhancements to the algorithm and the incorporation of advanced metrics could improve its performance, facilitating broader clinical applications for diagnosing complex shoulder conditions.
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