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International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(1): 11-22
Published online March 25, 2022
https://doi.org/10.5391/IJFIS.2022.22.1.11
© The Korean Institute of Intelligent Systems
EMG Pattern Recognition Using TFD for Future Control of In-Car Electronic Equipment
Ezreen Farina Shair1, Radhi Hafizuddin Razali1, Abdul Rahim Abdullah1, and Nurul Fauzani Jamaluddin2
1Rehabilitation and Assistive Technology Research Group, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
2Center for Innovation in Medical Engineering, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
Correspondence to :
Ezreen Farina Shair (ezreen@utem.edu.my)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: Electromyography, Time-frequency distribution, Spectrogram, Machine learning, Support vector machine, Pattern recognition
Acknowledgments
Conflict of Interest
No potential conflict of interest relevant to this article was reported.
E-mail: ezreen@utem.edu.my
E-mail: radhihafizuddin@gmail.com
E-mail: abdulr@utem.edu.my
E-mail: nfauzani@um.edu.my
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Original Article
International Journal of Fuzzy Logic and Intelligent Systems 2022; 22(1): 11-22
Published online March 25, 2022 https://doi.org/10.5391/IJFIS.2022.22.1.11
Copyright © The Korean Institute of Intelligent Systems.
EMG Pattern Recognition Using TFD for Future Control of In-Car Electronic Equipment
Ezreen Farina Shair1, Radhi Hafizuddin Razali1, Abdul Rahim Abdullah1, and Nurul Fauzani Jamaluddin2
1Rehabilitation and Assistive Technology Research Group, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
2Center for Innovation in Medical Engineering, Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
Correspondence to:Ezreen Farina Shair (ezreen@utem.edu.my)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: Electromyography, Time-frequency distribution, Spectrogram, Machine learning, Support vector machine, Pattern recognition
Fig 1.
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Graph of time-frequency domain.
Fig 2.
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Classes of pointing fingers.
Fig 3.
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Classification learning workflow.
Fig 4.
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Raw EMG signal of I-I movement for Participant 1.
Fig 5.
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Filtered EMG signal of I-I movement for Participant 1.
Fig 6.
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RMS voltage of I-I movement for Participant 1.
Fig 7.
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Average RMS voltage for each movement.
Fig 8.
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Scatter plots of E1 and E2.
Fig 9.
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Confusion matrix of SVM classifier.
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Table 1 . Advantages and disadvantages of various feature extraction techniques.
Features domain Advantage Disadvantage Time domain [10] Low noise environments Lower computational complexity Nonstationary property of EMG signal Statistical properties change over time Frequency domain [11] Reduces interference Good signal localization Clean signals High-noise environment Time-frequency domain [12] Overcomes limitations of time-domain features High dimensionality High resolution of feature vectors
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Table 2 . Advantages and disadvantages of various classifiers.
Classifier Advantage Disadvantage Support vector machine (SVM) Performs relatively well when a clear margin of separation exists between classes Does not perform well when dataset contains a high amount of noise K-nearest neighbor (KNN) Simple implementation Lazy learner Decision tree Does not require data scaling Expensive; time required to train the model is long Fuzzy Performs well with noise Not always accurate because predictions are based on assumptions
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Table 3 . Classes for 14 finger movements.
Class Movement L-L Little R-R Ring M-M Middle I-I Index T-T Thumb T-I Thumb index T-M Thumb middle T-R Thumb ring T-L Thumb little I-M Index middle M-R Middle ring R-L Ring little I-P Index pointing IMP Index middle pointing
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Table 4 . Highest average RMS voltage for each movement.
Class Highest average Vrms (V) Electrode I-I 7.53−7 E1 I-M 1.36−6 E1 IMP 1.15−6 E1 I-P 4.7−7 E2 L-L 4.52−6 E3 M-M 1.29−7 E5 M-R 5.58−7 E4 R-L 5.45−6 E3 R-R 1.83−6 E4 T-I 6.59−7 E1 T-L 1.79−6 E1 T-M 1.14−6 E1 T-R 1.96−6 E4 T-T 1.82−6 E2
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Table 5 . Comparison of classification performance.
Classifier Accuracy (%) Support vector machine (SVM) 89.3 Decision tree 57.1 Linear discriminant 34.5 K-nearest neighbor (KNN) 27.4
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