J Med Syst - Super wavelet for sEMG signal extraction during dynamic fatiguing contractions.

Tópicos

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Resumo

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%.

Resumo Limpo

research algorithm develop classifi muscl fatigu content dynam contract use genet algorithm ga pseudowavelet function fatigu dynam contract bicep brachii record use surfac electromyographi semg thirteen subject label signal two class fatigu nonfatigu aid train test phase genet algorithm use develop pseudowavelet function can optim decompos semg signal classifi fatigu content signal evolv pseudo wavelet tune use decomposit semg trial independ pseudowavelet evolut run best run select test remain trial measur classif perform result show evolv pseudowavelet improv classif rate muscl fatigu percentag point percentag point compar standard wavelet function p give averag correct classif

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