Hand movement recognition from sEMG signals using Fourier decomposition method

Fatimah, Binish; Singh, Pushpendra; Singhal, Amit; Pachori, Ram Bilas

doi:10.1016/j.bbe.2021.03.004

Cited by 40 publications

(14 citation statements)

References 47 publications

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“…We also perform statistical analysis of the Kruskal–Wallis test on feature space with dimension 30 ( P < 0.05 indicates the statistical difference between features) [ 53 ]. As shown in Table 3 , there is a statistical difference between the 30-dimensional features obtained by PCA dimensionality reduction.…”

Section: Resultsmentioning

confidence: 99%

“…Each subject repeats these hand movements 30 times. More details of the data set are introduced in the research [53][54][55]. Figure 2 shows the sEMG signal samples analyzed in this work.…”

Section: Methodsmentioning

confidence: 99%

“…(3) Bagging . Bagging is implemented based on the bootstrap sampling method; that is, random sampling with replacement is performed on a given training set and the obtained m subsets of the same size are used as the new training set [ 53 , 61 ]. Train the basic classification algorithm on these m training sets to get m models.…”

Section: Methodsmentioning

confidence: 99%

“…A comprehensive comparison of the classifcation accuracy of hand movements in fve diferent low-dimensional feature spaces shows that the 30-dimensional feature space achieves the best classifcation performance and, combined with the KNN classifer, has the highest classifcation accuracy of 96.03%. We also perform statistical analysis of the Kruskal-Wallis test on feature space with dimension 30 (P < 0.05 indicates the statistical diference between features) [53]. As shown in Table 3, there is a statistical diference between the 30-dimensional features obtained by PCA dimensionality reduction.…”

Section: Evaluation Of Semg Signal Classifcation Performancementioning

confidence: 99%

“…The extracted feature vectors are composed of MFCCs, and then, a generalized regression neural network (GRNN) is used to classify basic hand movements. Fatimah et al [ 53 ] proposed an automatic recognition algorithm for hand movements based on the Fourier decomposition method (FDM). The method adopts FDM to decompose the sEMG signal into Fourier intrinsic band functions (FIBFs).…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

Huo

Qin

et al. 2022

Computational Intelligence and Neuroscience

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As an input method of signal language, the hand movement classification technology has developed into one of the ways of natural human-computer interaction. The surface electromyogram (sEMG) signal contains abundant human movement information and has significant advantages as the input signal of human-computer interaction. However, how to effectively extract components from sEMG signals to improve the accuracy of hand motion classification is a difficult problem. Therefore, this work proposes a novel method based on wavelet packet transform (WPT) and principal component analysis (PCA) to classify six kinds of hand motions. The method applies WPT to decompose the sEMG signal into multiple sub-band signals. To efficiently extract the intrinsic components of the sEMG signal, the classification performance of different wavelet packet basis functions is evaluated. The PCA algorithm is used to reduce the dimension of the feature space composed of the features reflecting hand motions extracted from each sub-band signal. Besides, to ensure higher classification performance while reducing the dimension of the feature space by the PCA algorithm, the classification performance of different dimensions of the feature space is compared. In addition, the effects of the variability of the sEMG signal and the size of the window on the proposed method are further analyzed. The proposed method was tested on the sEMG for Basic Hand Movements Data Set and achieved an average accuracy of 96.03%. Compared with the existing research, the proposed method has better classification performance, which indicates that the research results can be applied to the fields of exoskeleton robot, rehabilitation training, and intelligent prosthesis.

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Section: Resultsmentioning

confidence: 99%

“…Each subject repeats these hand movements 30 times. More details of the data set are introduced in the research [53][54][55]. Figure 2 shows the sEMG signal samples analyzed in this work.…”

Section: Methodsmentioning

confidence: 99%