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- W3176445837 abstract "Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with a high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)." @default.
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- W3176445837 date "2021-10-07" @default.
- W3176445837 modified "2023-09-23" @default.
- W3176445837 title "EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine" @default.
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- W3176445837 doi "https://doi.org/10.1109/biocas49922.2021.9644978" @default.
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