Work

EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine

Year: 2021

Type: conference-paper

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).

Source: IEEE Biomedical Circuits and Systems Conference (BioCAS)

DOI: 10.1109/biocas49922.2021.9644978

Authors: Reza Bagherian Azhiri, Mohammad Esmaeili, Mohsen Jafarzadeh, Mehrdad Nourani

Institutions: The University of Texas at Dallas, University of Colorado Colorado Springs

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3176445837

FWCI: 7.075

Cites: 41

Cited by: 20

Related to: 10