Truncation effects on muscular fatigue indexes based on M waves analysis

Area of Science:

• Biomedical Engineering
• Neuroscience
• Physiology

Background:

• Muscular fatigue assessment is crucial for understanding muscle function and performance.
• Existing fatigue indexes rely on electromyogram (EMG) signals during electrically stimulated contractions (ES).
• Real-time analysis of muscle fatigue is challenging with current methods.

Purpose of the Study:

• To propose and validate a new fatigue index based on Continuous Wavelet Transform (CWT).
• To compare the performance of the CWT-based fatigue index against traditional indexes.
• To evaluate the sensitivity of fatigue indexes to noise and signal truncation.

Main Methods:

• Acquisition of muscle electrical activity (EMG) during electrically stimulated contractions (ES).
• Application of Continuous Wavelet Transform (CWT) for novel fatigue index calculation.
• Comparison of CWT-based index with standard fatigue indexes from literature.
• Analysis of index sensitivity to noise and M-wave truncation.

Main Results:

• The proposed CWT-based fatigue index shows comparable or superior performance to existing indexes.
• The CWT method demonstrates robustness against signal noise and M-wave truncation.
• Real-time analysis capabilities were maintained with the new index.

Conclusions:

• The CWT-based fatigue index offers a promising alternative for objective and reliable muscle fatigue assessment.
• This novel index enhances the accuracy and robustness of EMG-based fatigue monitoring.
• The findings support the use of CWT in analyzing physiological signals for real-time applications.