A Novel Electromyography (EMG) Based Classification Approach for Arabic Handwriting

Author First name, Last name, Institution

Azzedine Lansari, Zayed University
Faouzi Bouslama, Zayed University
Mohammed Khasawneh, Zayed University
Akram Al-Rawi, Zayed University

Document Type

Conference Proceeding

Source of Publication

Proceedings of the International Joint Conference on Neural Networks

Publication Date

9-24-2003

Abstract

In this paper, a novel classification approach for handwritten Arabic characters is proposed. Features for classification are extracted from electromygraphic (EMG) signals detected on two forearm muscles. Noise cancellations in conjunction with a process parameter estimator for feature identification are proposed. Neural networks using a potentially damped least mean squared algorithm is used at the classification stage. The proposed new classification technique is used on handwritten Arabic characters.

DOI Link

10.1109/ijcnn.2003.1223748

Publisher

IEEE

Volume

3

First Page

2193

Last Page

2196

Disciplines

Computer Sciences

Keywords

Algorithms, Feature extraction, Muscle, Neural networks, Neural disorders, Electromyography

Scopus ID

0141857585

Recommended Citation

Lansari, Azzedine; Bouslama, Faouzi; Khasawneh, Mohammed; and Al-Rawi, Akram, "A Novel Electromyography (EMG) Based Classification Approach for Arabic Handwriting" (2003). All Works. 199.
https://zuscholars.zu.ac.ae/works/199

Indexed in Scopus

yes

Open Access

no