JMR Cover
Frequency: Continuous
ISSN: 2617-0345 (Print)
ISSN: 2617-0353 (Online)
Research Article Open Access

Design and Implementation of Myoelectric Controlled Arm

Tariq M. Younes1, Mohammad A. AlKhedher2, Abdel-Hamid Soliman3 and Aiman Al Alawin1
  • 1 Al Balqa Applied University, Jordan
  • 2 Abu Dhabi University, United Arab Emirates
  • 3 Staffordshire University, United Kingdom

Abstract

In this study a discrimination system, using a neural network for Electromyogram (EMG) externally controlled Arm is proposed. In this system, the Artificial Neural Network (ANN) is used to learn the relation between the power spectrum of EMG signal analysed by Fast Fourier Transform (FFT) and the performance desired by handicapped people. The Neural Network can discriminate 4 performances of the EMG signals simultaneously. The digital signal processing was realized using MATLAB and LabVIEW software.

Journal of Mechatronics and Robotics
Volume 3 No. 1, 2019, 552-562

DOI: https://doi.org/10.3844/jmrsp.2019.552.562

Submitted On: 21 August 2019 Published On: 16 September 2019

How to Cite: Younes, T. M., AlKhedher, M. A., Soliman, A. & Al Alawin, A. (2019). Design and Implementation of Myoelectric Controlled Arm. Journal of Mechatronics and Robotics, 3(1), 552-562. https://doi.org/10.3844/jmrsp.2019.552.562

Open AccessCopyright: © 2019 Tariq M. Younes, Mohammad A. AlKhedher, Abdel-Hamid Soliman and Aiman Al Alawin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 3,118 Views
  • 1,726 Downloads
  • 0 Citations

Download

Keywords

  • Electromyogram
  • Neural Network
  • Biosignal
  • Gripping and Rotating