UC San Diego

Marker-based motion analysis is used to evaluate human movement and identify biomechanical features related to injury risk. However, these studies require extensive expertise, are labor intensive, time consuming, and high cost, making them impractical for routine use. Open-source smartphone-based motion capture systems such as OpenCap offer scalable, low cost, and automated alternatives to conventional motion capture systems. The goal of this study was to compare OpenCap to gold standard clinical marker-based motion capture for movement screening tasks. Sixty-two retroreflective markers were placed on ten healthy collegiate female athletes who completed a set of tasks commonly used to assess movement quality. A musculoskeletal model was used to estimate hip adduction, hip flexion, hip rotation, knee flexion, and ankle flexion with both OpenCap and marker-based inverse kinematics. OpenCap was approximately 12 times faster for data collection and processing, with only 2% of trials deemed unusable. RMSEs ranged from 3.5 to 11.3° across all joints and movement tasks. Pearson’s correlation coefficients for peak joint angles for select movements ranged from moderate to very strong for hip, knee, and ankle flexion and weak to very strong for hip adduction and hip rotation. Bland Altman plots showed varying trends, bias, and limits of agreement depending on the movement and joint angle. This suggests that smartphone markerless motion capture offers a time and cost-effective alternative for capturing movement and should be further evaluated for suitability to specific clinical questions. Moreover, this platform can be further developed to increase accuracy with enhanced training of algorithms and become a standard tool for routine, high-throughput movement screening in athletes.