标题:An Adaptation Strategy of Using LDA Classifier for EMG Pattern Recognition
作者:Zhang, Haoshi; Zhao, Yaonan; Yao, Fuan; Xu, Lisheng; Shang, Peng; Li, Guanglin
作者机构:[Zhang, Haoshi; Shang, Peng; Li, Guanglin] Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Hlth Engn, Shenzhen 518055, Guangdong, Peoples R 更多
会议名称:35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)
会议日期:JUL 03-07, 2013
来源:2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
出版年:2013
页码:4267-4270
DOI:10.1109/EMBC.2013.6610488
摘要:The time-varying character of myoelectric signal usually causes a low classification accuracy in traditional supervised pattern recognition method. In this work, an unsupervised adaptation strategy of linear discriminant analysis (ALDA) based on probability weighting and cycle substitution was suggested in order to improve the performance of electromyography (EMG)-based motion classification in multifunctional myoelectric prostheses control in changing environment. The adaptation procedure was firstly introduced, and then the proposed ALDA classifier was trained and tested with surface EMG recordings related to multiple motion patterns. The accuracies of the ALDA classifier and traditional LDA classifier were compared when the EMG recordings were added with different degrees of noise. The experimental results showed that compared to the LDA method, the suggested ALDA method had a better performance in improving the classification accuracy of sEMG pattern recognition, in both stable situation and noise added situation.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:17
Scopus被引频次:24
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886474773&doi=10.1109%2fEMBC.2013.6610488&partnerID=40&md5=686b9497892598ca1c3d26f3e97231ca
TOP