Using textural spectral feature extraction and support vector machine for EMG physical actions classification

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Tabatabaei Sayed Mohamad; Chalechale Abdolah

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Journal: MACHINE VISION AND IMAGE PROCESSING Year:2018 | Volume:5 | Issue:1 Page(s): 15-28

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Information Journal Paper

Title

Using textural spectral feature extraction and support vector machine for EMG physical actions classification

Author(s)

Tabatabaei Sayed Mohamad | Chalechale Abdolah | Issue Writer Certificate

Keywords

Time-frequency image (TFI)

Local Binary Pattern (LBP)

Gray Level Co-occurrence Matrix (GLCM)

Electromyography (EMG)

Support vector machine (SVM)

Abstract

Electromyographs are used for electromyography signal extraction from neurologically activated muscle cells. These signals are investigated to extract discriminating patterns to be categorized in the classification stage of myoelectric control systems (MCSs) designed for various applications. Feature extraction is a fundamental step in EMG signal processing which affects the overall performance of MCSs. To improve classification accuracy of MCSs, this paper proposes a novel approach for feature extraction from time-frequency images of EMG signals using local binary patterns and gray level co-occurrence matrices. In contrast to time alone and frequency alone approaches, by textural analysis of EMG signal spectrogram, time-frequency patterns of these signals are revealed, simultaneously. Furthermore, LBP and GLCM expose relational properties of time-frequency patterns which areexploited as the main features for classification. EMG physical action dataset is utilized in this study to evaluate the proposed method. In the classification stage, support vector machine classifiers are used in two segmented and holistic modes. The best classification accuracy of 98. 75% is obtained by segmented approach which is superior to the results provided by state of the art methods.

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APA: Copy

Tabatabaei, Sayed Mohamad, & Chalechale, Abdolah. (2018). Using textural spectral feature extraction and support vector machine for EMG physical actions classification. MACHINE VISION AND IMAGE PROCESSING, 5(1 ), 15-28. SID. https://sid.ir/paper/393921/en

Vancouver: Copy

Tabatabaei Sayed Mohamad, Chalechale Abdolah. Using textural spectral feature extraction and support vector machine for EMG physical actions classification. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2018;5(1 ):15-28. Available from: https://sid.ir/paper/393921/en

IEEE: Copy

Sayed Mohamad Tabatabaei, and Abdolah Chalechale, “Using textural spectral feature extraction and support vector machine for EMG physical actions classification,” MACHINE VISION AND IMAGE PROCESSING, vol. 5, no. 1 , pp. 15–28, 2018, [Online]. Available: https://sid.ir/paper/393921/en

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