SVM and ANN based Classification of EMG signals by using PCA and LDA
- URL: http://arxiv.org/abs/2110.15279v1
- Date: Fri, 22 Oct 2021 06:44:08 GMT
- Title: SVM and ANN based Classification of EMG signals by using PCA and LDA
- Authors: Hritam Basak, Alik Roy, Jeet Bandhu Lahiri, Sayantan Bose, Soumyadeep
Patra
- Abstract summary: Myoelectric signals (MES) are generated in the muscles of the human body as unidimensional patterns.
Support Vector Machines (SVM) is a technique whose primary function is to identify an n-dimensional hyperplane to separate a set of input feature points into different classes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent decades, biomedical signals have been used for communication in
Human-Computer Interfaces (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. Because of this, the methods and
algorithms developed for pattern recognition in signals can be applied for
their analyses once these signals have been sampled and turned into
electromyographic (EMG) signals. Additionally, in recent years, many
researchers have dedicated their efforts to studying prosthetic control
utilizing EMG signal classification, that is, by logging a set of MES in a
proper range of frequencies to classify the corresponding EMG signals. The
feature classification can be carried out on the time domain or by using other
domains such as the frequency domain (also known as the spectral domain), time
scale, and time-frequency, amongst others. One of the main methods used for
pattern recognition in myoelectric signals is the Support 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. This technique
has the potential to recognize complex patterns and on several occasions, it
has proven its worth when compared to other classifiers such as Artificial
Neural Network (ANN), Linear Discriminant Analysis (LDA), and Principal
Component Analysis(PCA). The key concepts underlying the SVM are (a) the
hyperplane separator; (b) the kernel function; (c) the optimal separation
hyperplane; and (d) a soft margin (hyperplane tolerance).
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