Comput Math Methods Med - The analysis of surface EMG signals with the wavelet-based correlation dimension method.

Tópicos

{ signal(2180) analysi(812) frequenc(800) }
{ featur(3375) classif(2383) classifi(1994) }
{ use(1733) differ(960) four(931) }
{ featur(1941) imag(1645) propos(1176) }
{ measur(2081) correl(1212) valu(896) }
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Resumo

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.

Resumo Limpo

mani attempt made effect improv prosthet system control classif surfac electromyograph semg signal recent develop methodolog extract effect featur still remain primari challeng previous studi demonstr semg signal nonlinear characterist studi combin nonlinear time seri analysi timefrequ domain method propos waveletbas correl dimens method extract effect featur semg signal semg signal first analyz wavelet transform correl dimens calcul obtain featur semg signal featur use input vector gustafsonkessel cluster classifi discrimin four type forearm movement result show four separ cluster correspond differ forearm movement third resolut level result classif accuraci two channel semg signal use indic propos approach can provid import insight nonlinear characterist timefrequ domain featur semg signal suitabl classifi differ type forearm movement compar exist method propos method exhibit robust higher classif accuraci

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