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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)

Received: July 6, 2021; Revised: September 21, 2021; Accepted: January 10, 2022

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

This project was fully funded by Universiti Teknikal Malaysia Melaka under the Short Term Grant Scheme (High Impact) (No. PJP/2020/FKE/HI19/S01717).

No potential conflict of interest relevant to this article was reported.

Ezreen Farina Shair is a senior lecturer at the Department of Electrical Engineering, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM). She received her B.Eng. (Electrical, Control & Instrumentation) (2009) and M. Eng. (Electrical-Mechatronics & Automatic Control) (2011) from Universiti Teknologi Malaysia (UTM) and Ph.D. in Electronics Engineering (2019) from Universiti Putra Malaysia (UPM). Her research interests include bio-signal processing, machine learning, deep learning, artificial intelligence, and the Internet of Things. Dr. Ezreen is currently an executive committee member of the IEEE Engineering in Medicine & Biology Society (IEEE-EMBS) Malaysia Chapter.

E-mail: ezreen@utem.edu.my

Radhi Hafizuddin Razali completed her B.Eng. In Electrical Engineering (Control, Instrumentation, and Automation) from Universiti Teknikal Malaysia Melak (UTeM) in 2020. She is currently pursuing his career in the telecommunication and network industry.

E-mail: radhihafizuddin@gmail.com

Abdul Rahim Abdullah received his B.Eng. (Electrical Engineering) (2001), M.Eng. (Electrical Engineering) (2004) and Ph.D. (Power Electronic and Digital Signal Processing) (2011) from Universiti Teknologi Malaysia (UTM). He is currently an Associate Professor in the Department of Electrical Engineering, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM). His field of specialization includes advanced digital signal processing, rehabilitation engineering, assistive technology and power electronics and drive. He is registered with the Board of Engineer Malaysia (BEM), Malaysia Board of Technologist (MBOT), Institute of Engineer Malaysia (IEM), and Members of International Association of Engineers (IAENG).

E-mail: abdulr@utem.edu.my

Nurul Fauzani Jamaluddin received her Bachelors in Electrical Engineering (Telecommunication) from Universiti Teknologi Malaysia in 2008. She obtained her Masters in Engineering Technology (Electric/Electronics) from Universiti Kuala Lumpur-British Malaysian Institute, and a Ph.D. in Biomedical Engineering from Universiti Putra Malaysia. Currently, she is a research officer at the Center for Innovation in Medical Engineering, Universiti Malaya. Her area of expertise is bio-instrumentation, bio-signal processing, artificial intelligence and Internet of Things.

E-mail: nfauzani@um.edu.my

Article

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)

Received: July 6, 2021; Revised: September 21, 2021; Accepted: January 10, 2022

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.

Figure 1.

Graph of time-frequency domain.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 2.

Figure 2.

Classes of pointing fingers.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 3.

Figure 3.

Classification learning workflow.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 4.

Figure 4.

Raw EMG signal of I-I movement for Participant 1.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 5.

Figure 5.

Filtered EMG signal of I-I movement for Participant 1.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 6.

Figure 6.

RMS voltage of I-I movement for Participant 1.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 7.

Figure 7.

Average RMS voltage for each movement.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 8.

Figure 8.

Scatter plots of E1 and E2.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Fig 9.

Figure 9.

Confusion matrix of SVM classifier.

The International Journal of Fuzzy Logic and Intelligent Systems 2022; 22: 11-22https://doi.org/10.5391/IJFIS.2022.22.1.11

Table 1 . Advantages and disadvantages of various feature extraction techniques.

Features domainAdvantageDisadvantage
Time domain [10]Low noise environmentsLower computational complexityNonstationary property of EMG signalStatistical properties change over time
Frequency domain [11]Reduces interferenceGood signal localizationClean signalsHigh-noise environment
Time-frequency domain [12]Overcomes limitations of time-domain featuresHigh dimensionalityHigh resolution of feature vectors

Table 2 . Advantages and disadvantages of various classifiers.

ClassifierAdvantageDisadvantage
Support vector machine (SVM)Performs relatively well when a clear margin of separation exists between classesDoes not perform well when dataset contains a high amount of noise
K-nearest neighbor (KNN)Simple implementationLazy learner
Decision treeDoes not require data scalingExpensive; time required to train the model is long
FuzzyPerforms well with noiseNot always accurate because predictions are based on assumptions

Table 3 . Classes for 14 finger movements.

ClassMovement
L-LLittle
R-RRing
M-MMiddle
I-IIndex
T-TThumb
T-IThumb index
T-MThumb middle
T-RThumb ring
T-LThumb little
I-MIndex middle
M-RMiddle ring
R-LRing little
I-PIndex pointing
IMPIndex middle pointing

Table 4 . Highest average RMS voltage for each movement.

ClassHighest average Vrms (V)Electrode
I-I7.53−7E1
I-M1.36−6E1
IMP1.15−6E1
I-P4.7−7E2
L-L4.52−6E3
M-M1.29−7E5
M-R5.58−7E4
R-L5.45−6E3
R-R1.83−6E4
T-I6.59−7E1
T-L1.79−6E1
T-M1.14−6E1
T-R1.96−6E4
T-T1.82−6E2

Table 5 . Comparison of classification performance.

ClassifierAccuracy (%)
Support vector machine (SVM)89.3
Decision tree57.1
Linear discriminant34.5
K-nearest neighbor (KNN)27.4

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