EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

TL;DR

This paper introduces a novel EMG signal classification method using reflection coefficients from AR models combined with the Extreme Value Machine, demonstrating superior accuracy over traditional classifiers like KNN and SVM.

Contribution

The paper presents a new approach that integrates reflection coefficients with EVM for improved EMG signal classification accuracy.

Findings

EVM outperforms KNN and SVM in accuracy.

Reflection coefficients effectively capture EMG features.

Proposed method enhances gesture recognition performance.

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).