This research aims to develop a reinforcement learning approach to map electromyography recordings to neural excitation effectively.
Developed a novel reinforcement learning-based mapping solution.
Focused on muscle-specific electromyography (MSp EMG) recordings and their challenges.
Evaluated the effectiveness in modeling musculoskeletal systems.
Demonstrated that the RL-based method effectively maps NMSp EMG to MSp neural excitation.
Highlighting potential increases in application for musculoskeletal models in challenging scenarios.
Abstract
Our RL-based approach is a novel, effective solution to map NMSp EMG recordings to MSp neural excitation. This method may broaden future applications of MSK models when recording MSp EMG is difficult.