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January 1, 2026IEEE Transactions on Biomedical Engineering

Utilizing Reinforcement Learning to Overcome the Challenge of Muscle-Specific EMG Placements for Musculoskeletal Modeling

Reilly Stafford Glenn Gaston Katherine R. Saul He Huang

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Key Points

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.

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Implication

Reveals effective mapping of EMG to neural excitation, indicating broader applications in musculoskeletal models.

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Cite This Study

Stafford et al. (2026) studied this question.

synapsesocial.com/papers/69edaafc4a46254e215b346d https://doi.org/10.1109/tbme.2026.3687060

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