Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

Saygin Siddiq Ahmed,Nuran Dogru,Ahmed R J Almusawi,Bülent Yilmaz

doi:10.5194/ms-12-69-2021

Saygin Siddiq Ahmed, Nuran Dogru + Show 2 more

Open Access

https://doi.org/10.5194/ms-12-69-2021

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Journal: Mechanical Sciences	Publication Date: Feb 4, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Gaziantep University, University of Baghdad

Abstract

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.

Highlights

There are around 50 000 amputation cases in the USA alone
One study has investigated the use of forearm surface electromyography signals acquired by three pairs of surface electrodes to classify arm movements
We remark that while our hand design is based on the ideas underlying the typically manufactured industrial robotic hand, we have added a servo motor and EMG-based grip control in order to improve the device performance as well as ensure weight reduction relative to the weights of previously manufactured hands

Summary

Introduction

There are around 50 000 amputation cases in the USA alone (as reported by the National Center for Health Statistics; Bhubaneswarr et al, 2007). Many amputees opt for prosthetic limbs (Hsu et al, 2006) In this context, one study has investigated the use of forearm surface electromyography (sEMG) signals acquired by three pairs of surface electrodes to classify arm movements. Along this research line, Raurale et al (2020) conducted real-time identification of active handmovement EMG signals based on wrist–hand mobility for simultaneous control of prosthetic robotic hands (Raurale and Chatur, 2014). In another system, a fully wireless, mobile platform used for acquisition and communication of sEMG signals is embedded in a mobile control system, and Ottobock 13E200 EMG electrodes are used to acquire the EMG signals.

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