Super wavelet for sEMG signal extraction during dynamic fatiguing contractions.

In this research an algorithm was developed to classify muscle fatigue content from dynamic contractions, by using a genetic algorithm (GA) and a pseudo-wavelet function. Fatiguing dynamic contractions of the biceps brachii were recorded using Surface Electromyography (sEMG) from thirteen subjects. Labelling the signal into two classes (Fatigue and Non-Fatigue) aided in the training and testing phase. The genetic algorithm was used to develop a pseudo-wavelet function that can optimally decompose the sEMG signal and classify the fatigue content of the signal. The evolved pseudo wavelet was tuned using the decomposition of 70% of the sEMG trials. 28 independent pseudo-wavelet evolution were run, after which the best run was selected and then tested on the remaining 30% of the trials to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.95 percentage points when compared to other standard wavelet functions (p<0.05), giving an average correct classification of 87.90%.