Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes
DOI:
https://doi.org/10.46243/jst.2020.v5.i4.pp248-260Keywords:
sEMG, multi-class movement, performance evaluationAbstract
:In the past few years of research done in the field of myoelectric control, many researchers have proposed several models imploying a combination of different features and classifiers to increase the movement classes, but all that work fails to explain if there is any correlation between multi-class classification and its accuracy. This paper focuses on finding the factors that decide the limit of movement classes that machine learning algorithms can accurately differentiate and to evaluate the performance of pattern classification techniques using the sEMG signal when the number of movement classes is increased while keeping the simplicity of the system. The results were obtained for eight channels sEMG signal using 7 independent time-domain features and four feature set combinations over 4 classifiers (Support Vector Machine(SVM), K-Nearest Neighbour(K-NN), Decision Tree(DT), and Naïve Bayes(NB)). Then the number of classes was increased in the manner of 5, 7, 10, 12, and 15 classes to determine the highest number of movement classes that the sEMG system with above-described features can classify efficiently. And the effect of increasing the number of movement classes on system accuracy was observed. The highest accuracies for all five class progression were obtained for SVM with the MFL feature, and for DT using MAV, it was successfully observed that the NB classifier had minimum performance depletion for the features used in this work
References
Abbaspour, S., Linden, M., GHolamhosseini, H., Naber, A., & Catalan, M. O. (2020). Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Medical & Biological Engineering & Computing, 83-100
Karlik, B. (2014). Machine learning Algorithms for Characterization of EMG Signal. International Journal of Information and Electronics Engineering, 189-191
Toledo-Pereze, D. C., Resendiz, R. J., Loenzo, R. A., & Jauregui-Correa, J. C. (2019). Support Vector Machine-Based EMG Signal Classification Techniques: A Review. MDPI, 1-28
Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., et al. (2019). Real-Time EMG Based Pattern Recognition Control for Hand Prosthesis: A Review on Exiting Challenges and Future Implementation. MDPI, 8-11
Sundarsan, S., & Sekaran, D. E. (2012). Design and Development of EMG Controlled Prosthetics Limb. Elevier Ltd., 3547-3549
Nazmi, N., Rahman, M. A., Yamamoto, S. I., Ahmad, S. A., Zamzuri, H., & Mazlan, S. A. (2016). A Review of Classification Techniques of EMG Signal during Isotonic and Isometric Contractions. MDPI, 8
Zhang, Y., Li, P., Zhu, X., SU, S. W., Guo, Q., XU, P., et al. (2017). Extracting time-frequency feature of single-channel vastut medialis EMG signals for knee exercise pattern recognition. Chengdu: PLOS ONE
Meena, C. (2019). EMG Feature Extraction and Classification Implementation on FPGA. Jaipur: MNIT
Burhan, N., Kasno, M., & Ghzali, R. (2016). Feature extraction of surface electromyography (sEMG) and signal processing technique in wavelet transform: a review. IEEE International Conference on Automatic Control and Intelligent Systems, 142-144
Igual, C., Pardo, L. A., Hahne, J. M., & Igual, J. (2019). Myoelectric Control of Upper Limb Prosthesis. MDPI, 6-8
Mane, S. M., Kambli, R. A., Kazi, P. F., & Singh, P. N. (2015). Hand Motion Recognition From Single Channel Surface EMG Using Wavelet & Artificial Neural Network. 4th International Conference on Advances in Computing, Communication and Control, 61
K., S. G., Sivanandan, K. S., & Mohandas, K. P. (2012). Fuzzy Logic and Probabilistic Neural Network for EMG Classification-A Comparative Study. International Journal of Engineering & Technology, 3
Subasi, A. (2012). Classification of EMG signal using combined features and soft computing techniques. ELSEVIER, 2188-2198
Phinyomark, A., HU, H., Phukpattaranont, P., & Limsakul, C. (2012). Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification. MEASUREMENT SCIENCE REVIEW, 83-84
Joyce, J. (2019, March 21). The Standford Encyclopedia of Philosophy. Retrieved May 24, 2020, from plato.standford.edu: <https://plato.stanford.edu/archives/spr2019/entries/bayes-theorem/>
Dev, R., & Singh, A. K. (2016). Performance analysis of classifiers for EMG signal of different hand movements. International journal of biomedical engineering and technology, 240-241
Wettschereck, D., Aha, D. W., & Mohri, T. (1997). A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazzy Algorithms. Artificial Intelligence Review, 275-277
Ray, S. (2017, September 13). Understanding Support Vector Machine(SVM) algorithm from examples (along with code). Retrieved May 25, 2020, from analyticsvidhya: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Waseem, M. (2019, November 27). A Quick Guide To Learn Support Vector Machine. Retrieved May 25, 2020, from edureka: https://www.edureka.co/blog/support-vector-machine-in-python/
Khushaba, R. N., Kodagoda, S., Liu, D., & Dissanayake, G. (2012). Electromyogram(EMG) Feature Reduction Using Mutual Components Analysis for Multifunctional Prosthetic Finger Control. Int. Conf. on Control, Automation, Robotics & Vision(ICARCV), 1534-1539
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