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• W2944099868 abstract "A surface electromyographic signal can provide information on neuromuscular activity and can be used as an input in a myoelectric control system for applications such as orthotic exoskeletons. In this process, a key step is to extract useful information from the EMG signals using the pattern recognition tools. Our research focus is on identification of a set of relevant features for efficient EMG signal classification. Specifically in this work, from the pre-processed myoelectric signals, we extracted auto regression coefficients, different time-domain features such as Hjorth features, integral absolute value, mean absolute value, root mean square and cepstral features. Next a subset consisting of a few selected features are fed to the multiclass SVM classifier. Using a radial basis function kernel a classification accuracy of 92.3% has been achieved." @default.
• W2944099868 created "2019-05-16" @default.
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• W2944099868 date "2018-10-01" @default.
• W2944099868 modified "2023-09-25" @default.
• W2944099868 title "A Machine Learning System for Classification of EMG Signals to Assist Exoskeleton Performance" @default.
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• W2944099868 doi "https://doi.org/10.1109/aipr.2018.8707426" @default.
• W2944099868 hasPublicationYear "2018" @default.
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