A Proof-of-Concept Comparison of Azure Kinect and Smartphone-based OpenCap for Driving Musculoskeletal Simulations: A Unified Framework with Foot-Ground Contact Modeling

Background: Marker-based motion capture remains the gold standard for deriving lower-limb kinematics and kinetics, but its high cost, lengthy setup time, and large space requirements limit its widespread use. Markerless technologies, such as depth camera systems (e.g., Azure Kinect) and emerging smartphone-based pipelines (e.g., OpenCap), promise laboratory-grade motion capture without markers. However, the effects of their capture data on musculoskeletal multibody dynamics simulation outcomes remain insufficiently understood. Methods: This study was aimed at developing a single lower-limb musculoskeletal model simultaneously driven by two markerless motion capture inputs (Azure Kinect and OpenCap), and benchmarking the resulting joint angles, ground-reaction forces, and joint contact forces against synchronous Vicon recordings. In gait trials, movements were simultaneously recorded with Azure Kinect, OpenCap, and a Vicon motion capture system. The collected data were processed and used as inputs to construct the musculoskeletal model, which was then combined with a foot-ground contact model to compute lower-limb joint angles, ground reaction forces (GRFs), and joint contact forces. Results: The OpenCap-based model showed strong agreement with the Vicon-referenced model ( ρ ρ > 0.73) in hip flexion-extension (FE), knee FE, and ankle FE, with a root mean square error (RMSE) of 4.04° to 7.66°, Sprague and Geers magnitude error ( M ) of −0.25 to −0.10, phase error ( P ) of 0.08 to 0.25, and composite error ( C ) of 0.16 to 0.39. Additionally, strong correlations ( ρ > 0.77) in hip contact force, knee contact force, medial knee contact force, and ankle contact force were observed between OpenCap and Vicon, with an RMSE of 0.26 to 0.90 Body Weight (BW), M of −0.03 to 0.13, P of 0.05 to 0.08, and C of 0.08 to 0.17. Conclusions: Overall, under the study conditions, the smartphone-based OpenCap preliminarily showed accuracy as a potential alternative to marker-based systems for estimating lower-limb biomechanics. However, given the small sample size and tasks restricted to walking, it is currently primarily suited for research settings or initial screening, rather than high-precision clinical diagnosis. Further studies in larger, more diverse cohorts and validation across dynamic activities are required to confirm and extend its applicability.