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

TLDR

The study proposes and evaluates using support vector machines to classify upper limb motions from myoelectric signals. The authors develop an SVM‑based myoelectric control system, optimizing data segmentation, feature selection, model tuning, and post‑processing (including overlapped segmentation and majority voting), comparing it to LDA and MLP classifiers while also using output entropy as an online performance index. The system achieves exceptional accuracy, robust performance, and low computational load.

Abstract

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.