Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

Khairul Anam; Harun Ismail; Faruq Sandi Hanggara; Cries Avian; Safri Nahela; Muchamad Arif Hana Sasono

doi:10.5614/j.eng.technol.sci.2023.55.5.8

Authors

Khairul Anam Dept. of Electrical Engineering, Faculty of Engineering, University of Jember, Kalimantan Street No. 37, Sumbersari, Krajan Timur, Sumbersari, Sumbersari District, Jember Regency, East Java 68121, Indonesia
Harun Ismail Dept. of Electrical and Computer, Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da?an District, Taipei City, Taiwan 106, Taiwan
Faruq Sandi Hanggara Dept. of Electrical Engineering, Faculty of Engineering, University of Jember, Kalimantan Street No. 37, Sumbersari, Krajan Timur, Sumbersari, Sumbersari District, Jember Regency, East Java 68121, Indonesia
Cries Avian Dept. of Electrical and Computer, Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd, Da?an District, Taipei City, Taiwan 106, Taiwan
Safri Nahela Center for Development of Advanced Science and Technology, University of Jember, Kalimantan Street No. 37, Sumbersari, Krajan Timur, Sumbersari, Sumbersari District, Jember Regency, East Java 68121, Indonesia
Muchamad Arif Hana Sasono Dept. of Electrical Engineering, Faculty of Engineering, University of Jember, Kalimantan Street No. 37, Sumbersari, Krajan Timur, Sumbersari, Sumbersari District, Jember Regency, East Java 68121, Indonesia

DOI:

https://doi.org/10.5614/j.eng.technol.sci.2023.55.5.8

Keywords:

classification, electromyography, feature extraction, finger movement, machine learning

Abstract

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Nae Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

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References

Aziz, F.A., Alsaeed, A.S.M.A., Sulaiman, S., Ariffin, M.K.A.M. & Al-Hakim, M.F., Mixed Reality Improves Education and Training in Assembly Processes, Journal of Engineering and Technological Sciences, 52(4), pp. 598-607, 2020.

Nguyen, H.T., Phan, T.T., Dao, T.C., Ta, P.V.D., Nguyen, C.N.T., Pham, N.H. & Huynh, H.X., Gene Family Abundance Visualization Based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis, Journal of Engineering and Technological Sciences, 53(1), pp. 1-17, 2021.

Sonawane, S., Sekhar, R., Warke, A., Thipse, S. & Varma, C., Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning, Journal of Engineering and Technological Sciences, 55(3), pp. 340-355, 2023.

Pan, L., Crouch, D. L. & Huang, H., Comparing EMG-based Human-machine Interfaces for Estimating Continuous, Coordinated Movements, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(10), pp. 2145-2154, 2019.

Khadivar, F., Mendez, V., Correia, C., Batzianoulis, I., Billard, A. & Micera, S., EMG-driven Shared Human-Robot Compliant Control for in-hand Object Manipulation in Hand Prostheses, Journal of Neural Engineering, 19(6), 2022.

Ambikapathy, B. & Krishnamurthy, K., Analysis of Electromyograms Recorded Using Invasive and Noninvasive Electrodes: A Study Based on Entropy and Lyapunov Exponents Estimated Using Artificial Neural Networks, Journal of Ambient Intelligence and Humanized Computing, pp. 1-9, 2018.

Barsakcioglu, D.Y. & Farina, D., A real-time Surface EMG Decomposition System for Non-invasive Human-Machine Interfaces, in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, pp. 1-4, 2018.

Arteaga, M.V., Castiblanco, J.C., Mondragon, I.F., Colorado, J.D. & Alvarado-Rojas, C., EMG-driven Hand Model Based on the Classification of Individual Finger Movements, Biomedical Signal Processing and Control, 58, 101834, 2020.

Buongiorno, D., Barsotti, M., Barone, F., Bevilacqua, V. & Frisoli, A., A Linear Approach to Optimize an Emg-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-control: A case study on Shoulder and Elbow Joints, Frontiers in Neurorobotics, 12, p. 74, 2018.

Huang, Y., Lai, W. P., Qian, Q., Hu, X., Tam, E.W.C. & Zheng, Y., Translation of Robot-assisted Rehabilitation to Clinical service: a Comparison of the Rehabilitation Effectiveness of EMG-driven Robot Hand Assisted Upper Limb Training in Practical Clinical Service and in Clinical Trial with Laboratory Configuration for Chronic Stroke, Biomedical Engineering Online, 17(1), pp. 1-17, 2018.

Liu, J., Ren, Y., Xu, D., Kang, S.H. & Zhang, L.-Q., EMG-based Real-time linear-nonlinear cascade Regression Decoding of Shoulder, Elbow, and Wrist Movements in Able-bodied Persons and Stroke Survivors, IEEE Transactions on Biomedical Engineering, 67(5), pp. 1272-1281, 2019.

Fleming, A., Stafford, N., Huang, S., Hu, X., Ferris, D.P. & Huang, H.H., Myoelectric Control of Robotic Lower limb prostheses: A Review of Electromyography Interfaces, Control Paradigms, Challenges and Future Directions, Journal of Neural Engineering, 18(4), 2021.

Tamilselvi, R., Merline, A., Parisa Beham, M., Vijay Anand, R., Shre Karthik, M. & Uthayakumar, R H., EMG Activated Robotic Arm for Amputees, Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, Icisc, pp. 456-461, 2018.

Manero, A. C., McLinden, S. L., Sparkman, J. & Oskarsson, B., Evaluating Surface EMG Control of Motorized Wheelchairs for Amyotrophic Lateral Sclerosis Patients, Journal of NeuroEngineering and Rehabilitation, 19(1), pp. 1-10, 2022.

Rabin, N., Kahlon, M., Malayev, S. & Ratnovsky, A., Classification of Human Hand Movements Based on EMG Signals Using Nonlinear Dimensionality Reduction and Data Fusion Techniques, Expert Systems with Applications, 149, 113281, 2020.

Waris, A., Niazi, I. K., Jamil, M., Englehart, K., Jensen, W. & Kamavuako, E.N., Multiday Evaluation of Techniques for EMG-based Classification of Hand Motions, IEEE Journal of Biomedical and Health informatics, 23(4), pp. 1526?1534, 2018.

Arteaga, M.V, Castiblanco, J.C., Mondragon, I.F., Colorado, J.D. & Alvarado-Rojas, C., EMG-driven Hand Model Based on the Classification of Individual Finger Movements, Biomedical Signal Processing and Control, 58, 101834, 2020.

Kim, S., Kim, J., Koo, B., Kim, T., Jung, H., Park, S., Kim, S. & Kim, Y., Development of an Armband EMG Module and a Pattern Recognition Algorithm for the 5-finger Myoelectric Hand Prosthesis, International Journal of Precision Engineering and Manufacturing, 20(11), pp. 1997-2006, 2019.

Toledo-Pez, D. C., Rodruez-Resdiz, J., Gez-Loenzo, R. A. & Jauregui-Correa, J. C., Support Vector Machine-based EMG Signal Classification Techniques: A Review, Applied Sciences, 9(20), 4402, 2019.

Sharif, H., Seo, S.B. & Kesavadas, T.K., Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals, Sensors, 2020(July), pp. 682-685, 2020.

Samuel, O.W., Zhou, H., Li, X., Wang, H., Zhang, H., Kumar, A. & Li, P., Pattern Recognition of Electromyography Signals Based on Novel Time Domain Features for Amputees? Limb Motion Classification, Computers and Electrical Engineering, 67, pp. 646-655, 2018.

Alger, A.E. & Ergezer, H., Window Length Insensitive Real-time EMG Hand Gesture Classification Using Entropy Calculated from Globally Parsed Histograms, Measurement and Control (United Kingdom), 56(7?8), pp. 1278-1291, 2023.

The Ninapro Team, Ninapro DB5 Guidelines. Ninapro, https://ninapro.hevs.ch/instructions/DB5.html, (16 October 2023).

Liao, Z., Chen, B., Bai, D., Xu, J., Zheng, Q., Liu, K.& Wu, H., Human?robot Interface Based on sEMG Envelope Signal for the Collaborative Wearable Robot, Biomimetic Intelligence and Robotics, 3(1), 100079, 2023.

Abbaspour, S., Lind, M., Gholamhosseini, H., Naber, A. & Ortiz-Catalan, M., Evaluation of Surface EMG-based Recognition Algorithms for Decoding Hand Movements, Medical & Biological Engineering & Computing, 58(1), pp. 83-100, 2020.

Too, J., Abdullah, A.R. & Saad, N.M., Classification of Hand Movements Based on Discrete Wavelet Transform and Enhanced Feature Extraction, Int. J. Adv. Comput. Sci. Appl, 10(6), pp. 83-89, 2019.

Bansal, M., Goyal, A. & Choudhary, A., A Comparative Analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory Algorithms in Machine Learning, Decision Analytics Journal, 3(May), 100071, 2022.

Phukpattaranont, P., Thongpanja, S., Anam, K., Al-Jumaily, A. & Limsakul, C., Evaluation of Feature Extraction Techniques and Classifiers for Finger Movement Recognition Using Surface Electromyography Signal, Medical & biological engineering & computing, 56(12), pp. 2259-2271, 2018.

Syed, S.A., Zahid, H., Bullo, S., Shams, S., Tanvir, S., Zaidi, S.J.H. & Nasim, S., Machine Learning Techniques Applied in Surface Emg Detection- a Systematic Review, Pakistan Journal of Biotechnology, 20(02), pp. 225-237, 2023.

Ghosh, A., SahaRay, R., Chakrabarty, S. & Bhadra, S., Robust Generalised Quadratic Discriminant Analysis, Pattern Recognition, 117, 107981, 2021.