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Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

Oskoei, MA and Huosheng Hu, (2008) 'Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb.' IEEE Transactions on Biomedical Engineering, 55 (8). pp. 1956-1965. ISSN 0018-9294

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This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations. © 2006 IEEE.

Item Type: Article
Uncontrolled Keywords: classification; data segmentation; entropy; feature selection; myoelectric control; support vector machine (SVM)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Mar 2013 14:50
Last Modified: 15 Jan 2022 00:26

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