Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces

The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (rho) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (rho = 0.74 similar to 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 similar to 6.56 degrees and 2.14 similar to 7.08 degrees, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (rho = 0.78 similar to 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 similar to 1.07 bodyweight (BW) and 0.28 similar to 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.