Classification of Five Finger Movement, based on a Low-cost, Real-time EMG System

Clive Seguna, Adrian Von Brockdorff, Jeremy Scerri, Kris Scicluna

2020

Abstract

Researchers commonly use myoelectric signals to study the electrical activity produced by skeletal muscles for the control of prosthetic arms, hands and limb replacement devices. Additionally, to the application in prostheses, a myoelectric control system for multiple finger movements has the potential to develop commercial products including advanced human-computer interfaces. The objective of this work is to implement a set of low-cost active electrodes for the decoding of finger movement via time-domain analysis, with an auto-gain adjustment technique. Different people will have different EMG amplitudes; therefore, it is difficult to determine the gain required prior performing further signal processing. In this work, an auto-adjustable gain amplifier circuit processes the maximum EMG signal amplitude and adjusts the gain stage accordingly, without the need of any user interaction. This ensures that the gain is always automatically adjusted to get the most effective performance from the data acquisition or analogue to digital converter (ADC) module since the signal will be neither too low in amplitude to cause inefficient use of the ADC resolution, nor too high to cause saturation of the signal. Through extensive experiments, the developed low-cost EMG data acquisition system achieves reproducible and repeatable results for the detection and classification of the five finger movements.

Paper Citation

in Harvard Style

Seguna C., Von Brockdorff A., Scerri J. and Scicluna K. (2020). Classification of Five Finger Movement, based on a Low-cost, Real-time EMG System. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES; ISBN 978-989-758-398-8, SciTePress, pages 149-159. DOI: 10.5220/0008978901490159

in Bibtex Style

@conference{biodevices20,
author={Clive Seguna and Adrian Von Brockdorff and Jeremy Scerri and Kris Scicluna},
title={Classification of Five Finger Movement, based on a Low-cost, Real-time EMG System},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES},
year={2020},
pages={149-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008978901490159},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES
TI - Classification of Five Finger Movement, based on a Low-cost, Real-time EMG System
SN - 978-989-758-398-8
AU - Seguna C.
AU - Von Brockdorff A.
AU - Scerri J.
AU - Scicluna K.
PY - 2020
SP - 149
EP - 159
DO - 10.5220/0008978901490159
PB - SciTePress