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arXiv · 2106.10561

EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

Reza Bagherian Azhiri·Mohammad Esmaeili·Mohsen Jafarzadeh·Mehrdad Nourani
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Abstract

Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with 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).

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Reza Bagherian Azhiri, Mohammad Esmaeili, Mohsen Jafarzadeh, Mehrdad Nourani. 2021-06-19. EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine. https://doi.org/10.1109/biocas49922.2021.9644978

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