IIUM Repository

Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions

Ibrahimy, Muhammad Ibn and Ahsan, Md. Rezwanul and Khalifa, Othman Omran (2013) Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions. Measurement Science Review, 13 (3). pp. 142-151. ISSN 1335 - 8871

[img] PDF (full text) - Published Version
Restricted to Repository staff only

Download (768kB) | Request a copy

Abstract

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals.

Item Type: Article (Journal)
Additional Information: 4637/30535
Uncontrolled Keywords: Electromyography, neural network, back-propagation, Levenberg-Marquardt algorithm, EMG signal classifier
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Depositing User: Dr Muhammad Ibrahimy
Date Deposited: 11 Jul 2013 11:45
Last Modified: 04 Oct 2013 18:46
URI: http://irep.iium.edu.my/id/eprint/30535

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year