IIUM Repository (IREP)

Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN)

Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2011) Electromygraphy (EMG) signal based hand gesture recognition using Artificial Neural Network (ANN). In: 4th International Conference on Mechatronics (ICOM'11), 17-19 May 2011, Kuala Lumpur.

[img] PDF (Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)) - Published Version
Restricted to Repository staff only

Download (831kB) | Request a copy

Abstract

Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. The purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. The EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. The conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 4637/5887 ISBN : 978-1-61284-435-0
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr Othman O. Khalifa
Date Deposited: 22 Nov 2011 14:28
Last Modified: 22 Nov 2011 15:11
URI: http://irep.iium.edu.my/id/eprint/5887

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year