A feasibility study of leveraging intermuscular coherence in EMG-driven neuromusculoskeletal modeling to improve muscle moment estimation

Current musculoskeletal models often oversimplify the neural strategies underlying muscle activation, potentially leading to unsatisfactory estimates of muscle forces. Numerous studies in motor control have established that the central nervous system synchronizes muscle activation by sending a common drive to synergistic muscles, measurable through intermuscular coherence – the frequency correlation between two EMG signals. As interest grows in understanding how muscles synchronize during movement coordination, leveraging intermuscular coherence into musculoskeletal models represents an innovative approach. This could enhance the accuracy of muscle effort estimation and introduce a physiologically meaningful component of motor control. In this study, we introduce a new method that decomposes EMG signals into common and independent components, informed by intermuscular coherence, and integrates them into an EMG-driven model to estimate muscle moments. Using data from twenty-four healthy subjects performing horizontal upper limb extensions, we estimated moments of the four main muscles actuating the elbow and compared these estimations with those from a traditional EMG-driven model informed by full-wave rectified signal envelopes. Our results demonstrate that incorporating intermuscular coherence significantly enhanced kinetic data tracking and improved the robustness of muscle moment estimations against variations in model parameters, addressing a major limitation of traditional EMG-driven models. Furthermore, antagonist muscle moments were more accurately represented, resulting in more realistic co-contraction index values.

By integrating neural control strategies via intermuscular coherence into musculoskeletal models, the proposed approach offers a more accurate representation of muscle coordination. We recommend that future neuromusculoskeletal models incorporate intermuscular coherence to improve physiological realism of muscle effort estimations.