Med Biol Eng Comput - Classification of surface electromyographic signals by means of multifractal singularity spectrum.

Tópicos

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Resumo

In order to effectively control a prosthetic system, considerable attempts have been made in recent years to improve the classification accuracy of surface electromyographic (SEMG) signals. However, the extraction of effective features is still a primary challenge for the classification of SEMG signals. This study tried to solve the problem by applying the multifractal analysis. It was found that the SEMG signals were characterized by multifractality during forearm movements and different types of forearm movements were related to different multifractal singularity spectra. To quantitatively evaluate the multifractal singularity spectra of the SEMG signals, the areas of the singularity spectrum curves were calculated by integrating the spectrum curves with respect to the singularity strengths. Our results showed that there were several separate clusters resulting from singularity spectrum areas of different forearm movements when two channels of SEMG signals were used in this experimental research, which demonstrated that the multifractal analysis approach was suitable for identifying different types of forearm movements. By comparing with other feature extraction techniques, the multifractal singularity spectrum approach provided higher classification accuracy in terms of the classification of SEMG signals.

Resumo Limpo

order effect control prosthet system consider attempt made recent year improv classif accuraci surfac electromyograph semg signal howev extract effect featur still primari challeng classif semg signal studi tri solv problem appli multifract analysi found semg signal character multifract forearm movement differ type forearm movement relat differ multifract singular spectra quantit evalu multifract singular spectra semg signal area singular spectrum curv calcul integr spectrum curv respect singular strength result show sever separ cluster result singular spectrum area differ forearm movement two channel semg signal use experiment research demonstr multifract analysi approach suitabl identifi differ type forearm movement compar featur extract techniqu multifract singular spectrum approach provid higher classif accuraci term classif semg signal

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