EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

• URL: http://arxiv.org/abs/2106.10561v1
• Date: Sat, 19 Jun 2021 19:12:59 GMT
• Title: EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
• Authors: Reza Bagherian Azhiri, Mohammad Esmaeili, Mohsen Jafarzadeh, and Mehrdad Nourani
• Abstract summary: We propose to utilize Extreme Value Machine 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.
• Score: 2.169919643934826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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