sEMG wavelet-based indices predicts muscle power loss during dynamic contractions

Area of Science:

• Exercise Physiology
• Biomedical Engineering
• Signal Processing

Background:

• Surface electromyography (sEMG) is crucial for assessing muscle activity.
• Estimating muscle power output during dynamic fatiguing exercise presents challenges.
• Traditional sEMG indices may not fully capture complex physiological changes.

Purpose of the Study:

• To evaluate novel sEMG indices derived from discrete wavelet transform (DWT).
• To assess the sensitivity of these indices in tracking exercise-induced changes in muscle power output.
• To determine the robustness of new wavelet indices against noise.

Main Methods:

• Fifteen trained subjects performed a dynamic leg press fatiguing protocol.
• sEMG signals were recorded from the vastus medialis muscle.
• New sEMG wavelet indices were computed alongside traditional parameters (MRV, F(med), FI(nsm5)) and compared.

Main Results:

• The novel wavelet indices demonstrated superior accuracy in mapping muscle power output changes during fatigue.
• A single new wavelet index explained 46.6% of performance variance; a combination explained 49.8%.
• Wavelet indices exhibited high robustness against additive white Gaussian noise across various SNRs.

Conclusions:

• Proposed sEMG wavelet indices are effective tools for monitoring muscle power output during high-loading, dynamic fatiguing tasks.
• These indices offer enhanced sensitivity and robustness compared to traditional methods.
• Wavelet-based sEMG analysis holds promise for improved understanding of muscle fatigue mechanisms.