Abstract
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.
Highlights
In recent decades, biomedical signals have been used for communication in Human–ComputerInterfaces (HCI) for medical applications; an instance of these signals are the myoelectric signals (MES), which are generated in the muscles of the human body as unidimensional patterns
The results show the classification accuracy of an available clinical EMG database, of 140 samples, with 95% effectiveness using a polynomial kernel of fifth order in an Support Vector Machines (SVM)
The results provided high classification accuracy at over 95%
Summary
Biomedical signals have been used for communication in Human–Computer. With the aim of carrying out the pattern recognition for myoelectric applications, a series of features is extracted from the myoelectric signal for classification purposes. Vector Machines (SVM) technique whose primary function is to identify an n-dimensional hyperplane to separate a set of input feature points into different classes. A compilation of the most outstanding works that combine different techniques based on SVM is presented in this paper. It includes a list of those features most commonly used in the time, frequency, time–frequency, and spatial domains for pattern recognition. Other applications of the SVM-based classifier are included in the last section
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
