Force estimation in fatigue condition using a muscle-twitch model during isometric finger contraction

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Science

Background:

• Muscle fatigue significantly impacts motor control and force production.
• Accurate force estimation is crucial for understanding muscle function and developing rehabilitation strategies.
• Surface electromyography (sEMG) is a non-invasive tool for assessing muscle electrical activity.

Purpose of the Study:

• To develop and validate a novel force estimation method using a muscle-twitch model derived from sEMG signals.
• To evaluate the performance of the proposed method in both non-fatigue and fatigue conditions.
• To compare the proposed method with a conventional sEMG-based force estimation technique.

Main Methods:

• A muscle-twitch model was characterized using sEMG features and measured forces during isometric index finger abduction in nine healthy subjects.
• Dynamic contractions (0-20% MVC) were used to characterize the twitch model, while static contractions (50% MVC) induced muscle fatigue.
• Muscle fatigue was identified by changes in the twitch model parameters (peak decrease, contraction time increase).
• Force estimation performance was assessed in non-fatigue and fatigue states and compared to the Mean Absolute Value (MAV) method.

Main Results:

• The muscle-twitch model effectively identified muscle fatigue, showing decreased twitch peak and increased contraction time.
• In non-fatigue conditions, the proposed method (0.90 ± 0.05) showed comparable performance to the MAV method (0.88 ± 0.06).
• In fatigue conditions, the proposed method demonstrated significantly improved performance (0.87 ± 0.05) compared to the MAV method (0.78 ± 0.09).

Conclusions:

• The proposed muscle-twitch model provides a robust method for estimating muscle force, particularly in the presence of fatigue.
• This sEMG-based approach offers improved accuracy over conventional methods for force estimation during fatiguing contractions.
• The findings have implications for monitoring muscle function, assessing fatigue, and guiding interventions in clinical and research settings.