EMG pattern recognition using TFD for future control of in-car electronic equipment

Shair, Ezreen Farina and Razali, Radhi Hafizuddin and Abdullah, Abdul Rahim and Jamaluddin, Nurul Fauzani (2022) EMG pattern recognition using TFD for future control of in-car electronic equipment. IInternational Journal of Fuzzy Logic and Intelligent Systems, 22 (1). pp. 11-22. ISSN 1598-2645

Text EMG PATTERN RECOGNITION USING TFD FOR FUTURE CONTROL OF IN-CAR ELECTRONIC EQUIPMENT.PDF Download (3MB)

Abstract

Distracted drivers contribute to motor vehicle accidents. The maneuvering of in-car electronic equipment and controls, which typically requires the driver’s hands to be off the wheel and eyes off the road, are important factors that distract drivers. To minimize the interference of such distractions, a new control method is presented for detecting and decoding human muscle signals, which is known as electromyography (EMG). It is associated with various fingertips and pressures, and allows the mapping of various commands to control in-car equipment without requiring hands off the wheel. The most important step to facilitate such a scheme is to extract a highly discriminatory feature that can be used to separate and compute different EMG-based actions. The aim of this study is to accurately analyze EMG signals and classify finger movements that can be used to control in-car electronic equipment using a time– frequency distribution (TFD). The average root mean square voltage of seven participants and fourteen different finger movements are extracted as EMG features using a TFD. Four machine learning classifiers, i.e., support vector machine (SVM), decision tree, linear discriminant, and K-nearest neighbor (KNN), are used to classify pointing finger classes. The overall accuracy of the SVM precedes that of the other classifiers (89.3%), followed by decision tree (57.1%), linear discriminant (34.5%), and KNN (27.4%). The findings of this study are expected to be used in real-time applications that require both time and frequency information. Integrating the EMG signal to control in-car electronic equipment is expected to reduce the number of motor vehicle crashes globally

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Time-frequency distribution, Spectrogram, Machine learning, Support vector machine, Pattern recognition
Divisions:	Faculty of Electrical Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	13 Apr 2023 15:52
Last Modified:	13 Apr 2023 15:52
URI:	http://eprints.utem.edu.my/id/eprint/26665
Statistic Details:	View Download Statistic

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