Electromyogram refinement using muscle synergy based regulation of uncertain information

Area of Science:

• Biomedical Engineering
• Neuroscience
• Biomechanics

Background:

• Electromyogram (EMG) signals often contain uncertainty due to technical issues like crosstalk and maximum voluntary contraction limitations.
• This uncertainty affects the accuracy of individual EMGs in representing actual muscle activations.

Purpose of the Study:

• To develop and evaluate a new EMG refinement method to address signal uncertainty.
• To improve the accuracy of muscle activation representation by reducing redundancy in EMG signals.

Main Methods:

• Proposed an EMG refinement framework based on EMG-driven torque estimation (EDTE) utilizing a musculoskeletal forward dynamic model.
• Incorporated the concept of muscle synergy to account for the synergistic contribution of both measured and unmeasured muscles to torque generation.
• Minimized changes in EMG signals during refinement to prevent overestimation and ensure stability.

Main Results:

• The muscle-synergy-based EDTE framework was designed to regulate uncertainty in individual EMGs, considering unmeasured muscles.
• The refinement process focused on minimizing changes in EMG signals to avoid overestimation.
• Evaluation using Bland-Altman plots demonstrated that the proposed EDTE minimizes bias in torque approximation compared to raw EMGs.

Conclusions:

• The developed EMG refinement method effectively regulates uncertainty in EMG signals.
• The approach optimizes torque estimation by minimizing bias and ensuring signal stability.
• This leads to more reliable and accurate representations of muscle activations from EMG data.