Robust Controller Electromyogram Prosthetic Hand with Artificial Neural Network Control and Position

Authors

  • Saygin Ahmed1, Aydin S. Ahmed2, Bülent Yilmaz3, Nuran Dogru1

DOI:

https://doi.org/10.37506/ijfmt.v14i2.2854

Keywords:

Arduino controller., Electromyography, Hand robot, Prosthetic hand.

Abstract

In this study, we proposed and designed a new control method for an electromyographically (EMG) controlled prosthetic hand. The objective is to increase the control efficiency of the human–machine interface and afford greater control of the prosthetic hand. The process works as follows: EMG biomedical signals acquired from Myoware sensors positioned on the relevant muscles are sent to the robot that consist of hand, Arduino and MATLAB program, which computes and controls the hand position in free space along with hand grasping operations. The Myoware device acquires muscle signals and sends them to the Arduino. The Arduino analyzes the received signals, based on which it controls the motor movement. In this design, the muscle signals are read and saved in a MATLAB system file. After program processing on the industrial hand which is applied by MATLAB simulation, the corresponding movement is transferred to the hand, enabling movements, such as, hand opening and closing according to the signal stored in the MATLAB system. In this study, hand and fingerprints were designed using a three-dimensional printer by separate recording finger and thumb signals. The muscle signals were then analyzed in order to obtain peak signal points and convert them into data. These results indicate the effectiveness of the proposed method and demonstrate the superiority of the method for amputees because of the improved controllability and perceptibility afforded by the design.

Author Biography

Saygin Ahmed1, Aydin S. Ahmed2, Bülent Yilmaz3, Nuran Dogru1

1College of Engineering- University of Gaziantep-Turkey, 2Technical College of Kirkuk- Northern Technical University-Iraq, 3College of Engineering -University of Abdullah gül, Turkey

Published

2020-04-29

How to Cite

Saygin Ahmed1, Aydin S. Ahmed2, Bülent Yilmaz3, Nuran Dogru1. (2020). Robust Controller Electromyogram Prosthetic Hand with Artificial Neural Network Control and Position. Indian Journal of Forensic Medicine & Toxicology, 14(2), 508-513. https://doi.org/10.37506/ijfmt.v14i2.2854