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).
@article{arxiv.2106.10561,
title = {EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine},
author = {Reza Bagherian Azhiri and Mohammad Esmaeili and Mohsen Jafarzadeh and Mehrdad Nourani},
journal= {arXiv preprint arXiv:2106.10561},
year = {2022}
}
Comments
Accepted for presentation in IEEE Biomedical Circuits and Systems Conference (BioCAS 2021)