A Novel Motor Unit-Driven Neuro-Musculoskeletal Model for Estimating Continuous Wrist Motion

Deciphering the intentions underlying movement based on motor unit (MU) activities poses an enduring challenge, thereby constraining our understanding of the intricate transition mechanism from microscopic neural commands to macroscopic movements. This investigation introduces an innovative MU-driven neuro-musculoskeletal (NMS) model, aimed at the continuous estimation of wrist movements. The presented model utilizes both MU firing and waveform information, incorporating a linear model and physiological twitch model for the computation of MU-specific neural excitations. Sequentially, these MU-specific neural excitations were amalgamated to generate muscle-tendon unit (MTU)-specific neural excitations, which serves as inputs for the subsequent musculoskeletal model to accomplish joint angle estimation. To evaluate the efficacy of this model, high-density surface electromyography data and angular information were procured from the forearms of four subjects engaged in wrist flexion-extension tasks. Data acquisition was performed with two high-density electrodes and a motion capture system. The angle estimation results revealed a marked superiority of the proposed model over two conventional NMS models, showcasing the lowest normalized root mean square error and the highest determination coefficient. This study propounds a groundbreaking approach for decoding joint movements from MU activities, holding the potential to propel the advancement of NMS models towards the control of multiple degrees of freedom.