Machine Learning-Based Surrogates of Musculoskeletal Models to Link Maintained Muscle Activations to Joint Angles

While electrical stimulation can restore functional movements in paralyzed patients, stimulation parameter tuning is mostly performed by clinicians through trial-and-error. Computational models may enable automatic parameter optimization on digital twins, substantially reducing the required testing. Nonetheless, current computational models of electrical stimulation do not consider how neural activation is converted into movement through the musculoskeletal system. Musculoskeletal models may fill this gap, but exploring a large set of candidate stimulation protocols requires solving the forward dynamics problem for a very large number of degrees of freedom. Here, we show that traditional machine learning can be used to train surrogates of musculoskeletal models to predict the steady-state value of a specific joint angle given a set of tetanic muscle activations. We show that such machine learningbased surrogate models enable accurate angle prediction across different joints and musculoskeletal models, while providing an acceleration of more than three orders of magnitude. Additionally, these models are interpretable, and feature importance analysis correctly identifies the agonist-antagonist muscle groups maximally influencing the movement of a specific joint. Machine learning-based acceleration of musculoskeletal models paves the way for their integration into computational models to predict and optimize the effects of electrical stimulation in motor neuroprostheses.