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

• Main Authors: Shair, Ezreen Farina, Razali, Radhi Hafizuddin, Abdullah, Abdul Rahim, Jamaluddin, Nurul Fauzani
• Format: Article
• Language: English
• Published: The Korean Institute of Intelligent Systems 2022
• Online Access: http://eprints.utem.edu.my/id/eprint/26665/2/EMG%20PATTERN%20RECOGNITION%20USING%20TFD%20FOR%20FUTURE%20CONTROL%20OF%20IN-CAR%20ELECTRONIC%20EQUIPMENT.PDF; http://eprints.utem.edu.my/id/eprint/26665/; https://www.ijfis.org/journal/view.html?uid=981&vmd=Full; http://doi.org/10.5391/IJFIS.2022.22.1.11
• Institution: Universiti Teknikal Malaysia Melaka

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