Support Vector Machine-Based EMG Signal Classification Techniques: A Review
2019
Diana C. Toledo-Pérez | Juvenal Rodríguez-Reséndiz | Roberto A. Gómez-Loenzo | J. C. Jauregui-Correa
This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.
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