Reduction Of Limb Position Invariant Of SEMG Signals For Improved Prosthetic Control Using Spectrogram

Shair, Ezreen Farina and Suleiman, Muhammad Fadhlin Ashraf and Abdullah, Abdul Rahim and Saharuddin, Nur Zawani and Nazmi, Nurhazimah (2021) Reduction Of Limb Position Invariant Of SEMG Signals For Improved Prosthetic Control Using Spectrogram. International Journal of Integrated Engineering, 13 (5). pp. 79-88. ISSN 2229-838X

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Abstract

Prostheses are artificial devices that replace a missing body part, which might be lost through injury, infection, or a condition present at birth. It is proposed to re-establish the normal functions of the missing body part and can be made by hand or with a computer-aided design. As per the World Health Organization, around 160,000 individuals in Malaysia are required to use prostheses. One of the elements that influence the current prosthesis control is that the variety in the limb position and normal use results in different electromyogram (EMG) signals with the same movement at various positions. Consequently, the objective of this study is to ensure that amputees can control their prosthetics in an exact manner regardless of their hand movement and limb position. The raw EMG signals are taken from eight different hand movements’ classes at five different limb positions and each of these hand movements has seven electrodes attach to it. This paper utilizes time-frequency distribution which is spectrogram to extract the EMG feature and six SVM classification learners; linear, quadratic, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian were compared to find the most reasonable one for this application. The analysis performance is then verified based on classification accuracy. From the results, the overall accuracy for the classification is 65% (linear), 87.5% (quadratic) and 97.5% (cubic), 100% (fine Gaussian), 70% (medium Gaussian, and 45% (coarse Gaussian), respectively. It is believed that the study could fill in as knowledge to improve conventional prosthetic control strategies.

Item Type:	Article
Uncontrolled Keywords:	Electromyogram, Hand movement, Limb position, Prosthetic, Support vector machine, Time-frequency distribution
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	21 Mar 2022 09:51
Last Modified:	21 Mar 2022 09:51
URI:	http://eprints.utem.edu.my/id/eprint/25801
Statistic Details:	View Download Statistic

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