Wrist Torque Estimation by Combining Motor Unit Discharges With Musculoskeletal Model

Abstract

OBJECTIVE: The application of electromyography (EMG) decomposition techniques in myoelectric control has gradually increased. However, most decomposition-based control schemes rely on machine learning, lacking interpretation of the biological mechanisms underlying movement generation and requiring large datasets for training. As neuromusculoskeletal modeling provides a promising alternative, this study proposes a decomposition-based musculoskeletal model for simultaneous and proportional myoelectric control. METHODS: Sixteen able-bodied subjects participated in two experiments involving isometric wrist contractions in two degrees of freedom (DoF). High-density surface EMG signals and torques were recorded simultaneously. The EMG signals were decomposed into motor unit action potential trains (MUAPts). We proposed four clustering methods (two activation-based and two action potential-based) to group MUAPts, from which three neural features were extracted as neural excitations and input to the musculoskeletal model. MAIN RESULTS: An activation-based clustering method with the twitch feature achieved a relatively high accuracy (R2 = 0.791 ± 0.101 and 0.622 ± 0.148 in the two experiments) with the highest smoothness (Roughness = 1.389 ± 0.211 and 1.140 ± 0.159). CONCLUSION AND SIGNIFICANCE: The proposed MUAPt-based musculoskeletal model achieved promising accuracy in estimating continuous 2-DoF wrist torques, providing a novel approach for understanding the neuromechanical properties of multi-DoF movements and advancing the development of dexterous rehabilitation and robotic control.