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Publication Type : Conference Proceedings
Source : In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp. 1-5. IEEE, 2019.
Url : https://ieeexplore.ieee.org/abstract/document/8882900
Campus : Amritapuri
School : School of Engineering
Department : Mechanical Engineering
Verified : No
Year : 2019
Abstract : Electromyography (EMG) is widely used for diagnosis in the field of bio-medical research. The EMG signal from the soleus muscle is utilized in this study to understand the effect of variational mode decomposition (VMD) in signal classification. In this work forward and backward walking in even and uneven surfaces with inclinations of 0° and 10° were considered. Ten time domain features were extracted from both raw surface EMG signal, and VMD decomposed EMG signal. Scatter plot of features in all walking condition is analyzed to understand the variation within the scatter group. The features extracted from VMD based components were shown to perform better in accuracy during Support Vector Machines (SVM) classification than the raw signal.
Cite this Research Publication : Akhil, V. M., Jobin Varghese, P. K. Rajendrakumar, and K. S. Sivanandan. "Effect of VMD decomposition of soleus muscle EMG in SVM classification." In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp. 1-5. IEEE, 2019.