Advertisement
Published in:
2013 | OriginalPaper | Chapter
62. SEMG Multi-Class Classification Based on S4VM Algorithm
Authors : Zhuojun Xu, Yantao Tian, Zhang Li, Yang Li
Published in: Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012
Publisher: Springer London
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
A method using small amount of labeled instants and large unlabeled ones simultaneously involved in the training during the sEMG classification obtained a better effect is strongly needed. This paper introduces the S4VM proposed by Li et al. into surface EMG pattern recognition with small labeled instants and extends to multi-class classification problems, which will represent the autoregressive model characteristic value of the human hand movements of the seven types of EMG signal as the object of classification. The experimental results show that the safety semi-supervised support vector machine is suitable for the multi-pattern classification of surface EMG signal with high accuracy and good robustness.