Muscle fatigue assessment through electrodermal activity analysis during isometric contraction

Area of Science:

• Physiology
• Biomedical Engineering
• Neuroscience

Background:

• Muscle fatigue assessment traditionally relies on electromyography (EMG).
• Autonomic Nervous System (ANS) dynamics during fatigue are not fully understood.
• Electrodermal activity (EDA) reflects sympathetic nervous system arousal.

Purpose of the Study:

• To investigate the relationship between muscle fatigue and Autonomic Nervous System (ANS) dynamics using electrodermal activity (EDA).
• To evaluate the efficacy of the cvxEDA model in analyzing EDA signals during isometric muscle contractions.
• To develop a pattern recognition system for classifying muscle fatigue based on EDA features.

Main Methods:

• 32 healthy subjects performed isometric biceps contractions.
• Electromyography (EMG) assessed muscle fatigue.
• Electrodermal activity (EDA) signals were recorded and analyzed using the cvxEDA model.
• Phasic and tonic EDA components were extracted for feature analysis.
• A K-Nearest Neighbors (KNN) classifier was employed for pattern recognition.

Main Results:

• Significant increases in phasic EDA features were observed in the fatigued group compared to the non-fatigued group.
• The cvxEDA model successfully decomposed EDA signals into meaningful components.
• The KNN classifier achieved 75.69% accuracy in discriminating between fatigued and non-fatigued subjects.
• Phasic EDA features proved to be significant indicators of muscle fatigue.

Conclusions:

• Electrodermal activity (EDA) serves as a valuable correlate of muscle fatigue.
• EDA provides complementary information to traditional EMG-based fatigue indices.
• The findings support the use of EDA for non-invasive muscle fatigue monitoring and ANS assessment.