SELECTION OF STRUCTURE AND HYPER-PARAMETERS OF SVM FOR EFFICIENT SOLUTION OF THE TASKS OF CLASSIFICATION OF ELECTROMYOGRAPHY SIGNALS
DOI:
https://doi.org/10.30888/2415-7538.2019-14-01-010Keywords:
electromyography signal (EMG), classification algorithms, support-vector machine (SVM), preprocessing and normalization data, SVM hyper-parameters, SVM kernelAbstract
Support vector machine (SVM) is a widely used machine learning method for the problem of classifying electromyography signals. The purpose of this work is to search for efficient algorithms for the classification of such signals with minimal equipment cos
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References
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References:
Guo, W., Sheng, X., Liu, H. and Zhu, X., 2017. Toward an enhanced human–machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Transactions on Human-Machine Systems, 47(4), pp.564-575;
Fang, Y., Liu, H., Li, G. and Zhu, X., 2015. A multichannel surface EMG system for hand motion recognition. International Journal of Humanoid Robotics, 12(02), p.1550011;
Quitadamo, L.R., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S. and Saggio, G., 2017. Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review. Journal of neural engineering, 14(1), p.011001;
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Sharma, S. and Kumar, G., 2012. Wavelet analysis based feature extraction for pattern classification from single channel acquired EMG signal. Elixir Online Journal, 50, pp.0320-1;
Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), pp.281-305.
