sEMG signal classification using SMO algorithm and singular value decomposition

Surface Electromyography (sEMG) signal analysis is a challenging task in neuroscience. The signal is associated with an activity of muscles in Human body. It is a part of how human can control the robotic arm for helping people with disabilities. In this paper, we propose a new method based on Singular Value Decomposition (SVD) and SMO algorithm for classifying sEMG signals into six basic hand movements. By this proposed method, SVD is adopted for feature extraction and SMO classifier is used for classifying sEMG signals into six classes of basic hand movements in five subjects. In preliminary experiment, we investigates the number of features that can yield the best performance in the classification and it is found that the optimal number of features is 50. For performance evaluation, five classifiers including Decision Tree, K-nearest neighbor, Naive Bayes, RBF, and SMO, with 10 fold cross-validation technique are adopted. The experimental results have shown that SMO algorithm with V2M-SVD feature extraction can achieve the best performance for the classification of basic hand movements.