A forward-inverse dynamics modeling framework for human musculoskeletal multibody system

Multibody musculoskeletal modeling of human gait has been proved helpful in investigating the pathology of musculoskeletal disorders. However, conventional inverse dynamics methods rely on external force sensors and cannot capture the nonlinear muscle behaviors. Meanwhile, the forward dynamics approach is computationally demanding and only suited for relatively simple tasks. This study proposed an integrated simulation methodology to fulfill the requirements of estimating foot-ground reaction force, tendon elasticity, and muscle recruitment optimization. A hybrid motion capture system, which combines the marker-based infrared device and markerless tracking through deep convolutional neural networks, was developed to track lower limb movements. The foot-ground reaction forces were determined by a contact model for soft materials, and its parameters were estimated using a two-step optimization method. The muscle recruitment problem was first resolved via a static optimization algorithm, and the obtained muscle activations were used as initial values for further simulation. A torque tracking procedure was then performed by minimizing the errors of joint torques calculated by musculotendon equilibrium equations and inverse dynamics. The proposed approach was validated against the electromyography measurements of a healthy subject during gait. The simulation framework provides a robust way of predicting joint torques, musculotendon forces, and muscle activations, which can be beneficial for understanding the biomechanics of normal and pathological gait.