Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and...

Area of Science:

• Biomedical Engineering
• Neuroscience
• Sports Science

Background:

• Muscle fatigue assessment is crucial for understanding neuromuscular function.
• Surface Electromyography (sEMG) provides valuable insights into muscle activity.
• Analyzing non-stationary sEMG signals during dynamic contractions presents a challenge.

Purpose of the Study:

• To analyze dynamic muscle contractions and fatigue in the biceps brachii.
• To evaluate the effectiveness of Synchrosqueezed Wavelet Transform (SST) for sEMG analysis.
• To identify reliable features for characterizing muscle fatigue from sEMG signals.

Main Methods:

• Surface Electromyography (sEMG) signals were recorded during dynamic fatiguing contractions.
• Synchrosqueezed Wavelet Transform (SST) was employed to decompose sEMG signals into time-frequency matrices.
• Singular Value Decomposition (SVD) was applied to extract features like Maximum Singular Value (MSV), Singular Value Entropy (SVEn), and Singular Value Energy (SVEr).

Main Results:

• Both Morlet and Bump wavelets, used with SST, effectively characterized non-stationary variations in sEMG signals.
• Increasing MSV and SVEr values correlated with the progression of muscle fatigue.
• Decreasing SVEn values indicated increasing randomness in the sEMG signal during fatigue.

Conclusions:

• The proposed approach using SST and singular value features can characterize dynamic muscle contractions.
• This method is capable of assessing muscle fatigue under various neuromuscular conditions.
• The study highlights the potential of advanced signal processing techniques for quantitative muscle function analysis.