Anterior cruciate ligament (ACL) injuries are among the most debilitating traumas in team sports, frequently resulting in long-term consequences. Despite extensive research, injury incidence remains unchanged, highlighting the need for enhanced biomechanical understanding. This study presents a novel pipeline integrating model-based image matching (MBIM) using injury video footage, with dynamic musculoskeletal simulations to reconstruct ground reaction forces (GRFs), center of pressure (CoP), and joint loads during ACL injuries. GRFs were estimated via dynamic simulations, while CoP was calculated using the zero moment point method from sidestep maneuvers performed by three healthy participants (Tegner score: 6). The root mean square error (RMSE) between model predictions and experimental measurements was calculated for joint kinematics, GRFs, CoP and joint loads. Additionally, a custom pipeline transferred Blender-processed kinematics (model-based image matching) to OpenSim. The methodology was applied to a real-world non-contact ACL injury in a professional soccer player. Validation yielded a mean RMSE of 11° (hip), 4.6° (knee), and 4.2° (ankle), 0.2 BW for GRFs and 64 mm for CoP and a mean joint loads RMSE of 0.92 Nm/kg across all degrees of freedom, demonstrating robustness despite requiring further refinement. A case study demonstrated the pipeline's ability to extract dynamic forces during ACL injuries. Peak vertical GRFs were five times lower (BW), while anteroposterior and mediolateral components were three times higher than literature values. Elevated mediolateral forces contribute to increased abduction moments at the knee and hip, consistent with biomechanical ACL injury models. This study establishes a foundation for extracting previously inaccessible biomechanical data.
Loddo et al. (Tue,) studied this question.
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