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Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions

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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 %.

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Correspondence to Mohamed R. Al-Mulla.

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This article is part of the Topical Collection on Mobile Systems

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Al-Mulla, M.R., Sepulveda, F. Super Wavelet for sEMG Signal Extraction During Dynamic Fatiguing Contractions. J Med Syst 39, 167 (2015). https://doi.org/10.1007/s10916-014-0167-1

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  • Genetic algorithms
  • Localised muscle fatiguen
  • EMG
  • Wavelet analysis
  • Pseudo wavelets