OBJECTIVE: Musculoskeletal dynamics influence the progression and rehabilitation of movement-related conditions. However, estimating whole-body dynamics using accessible tools, like smartphone video, remains challenging. Physics-based and machine learning (ML)-based dynamic predictions each offer advantages, but both approaches struggle to achieve high accuracy and physical realism. Here, we created a hybrid ML-simulation framework to improve estimates of ground reaction forces, joint moments, and joint contact forces from smartphone video kinematics. METHODS: We used ML models to predict ground forces and centers of pressure from video-based kinematics. The hybrid framework generates a dynamic simulation that tracks predicted forces and kinematics while encouraging dynamic consistency. We compared the hybrid model with kinematic-tracking simulations and with ML-predicted forces applied via inverse dynamics. Performance was evaluated using mean absolute error relative to lab-based inverse dynamics using marker and force plate data from 10 individuals walking. RESULTS: The ML and hybrid approaches reduced vertical ground force error by 40-44% compared to simulation. The hybrid model improved joint moment accuracy by 29-45% and joint contact force accuracy by 12-13% compared to simulation- or ML-only approaches, with the largest improvements in peak medial knee contact force (49%) and knee adduction moment impulse (30%). CONCLUSION: Our hybrid model improves the accuracy of dynamics from smartphone videos during walking, outperforming simulation for ground forces and both simulation- and ML-only approaches for joint moments and contact forces. SIGNIFICANCE: These methods enable more accurate, scalable assessments of musculoskeletal dynamics, supporting out-of-lab studies and precision treatment of gait-related conditions.
Miller et al. (Thu,) studied this question.