Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Physiology

Background:

• Surface electromyography (sEMG) records neuromuscular electrical activity.
• sEMG signals are complex and extracting muscle force information is challenging.
• Understanding fatigue effects on sEMG is crucial for various applications.

Purpose of the Study:

• To generate synthetic sEMG signals for varying muscle force levels.
• To simulate sEMG under isometric non-fatigue and fatigue conditions.
• To analyze the characteristics of generated sEMG signals during fatigue.

Main Methods:

• Developed a feedback model integrating current distribution and volume conductor theory.
• Implemented a control algorithm for rate coding and firing pattern generation.
• Simulated sEMG signal generation for different force magnitudes and fatigue states.

Main Results:

• Generated synthetic sEMG signals exhibiting high complexity in both non-fatigue and fatigue states.
• Observed increased amplitude and decreased frequency in sEMG signals under fatigue.
• Validated the model's ability to replicate fatigue-induced changes in sEMG.

Conclusions:

• The developed model successfully generates realistic sEMG signals.
• Fatigue significantly alters sEMG signal characteristics, notably increasing amplitude and reducing frequency.
• This model serves as a valuable tool for investigating sEMG signal behavior under fatigue and non-fatigue conditions.