文章基本信息

标题：Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern
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作者：Kim, Deok-Hwan ; Cho, Chi-Young ; Ryu, Jaehwan 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2014
卷号：36
期号：1
页码：99-105
DOI：10.4218/etrij.14.0113.0064
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% ( ) when walking on a level surface, 96.47% ( ) when going up stairs, and 96.37% ( ) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.
关键词：EMG;locomotion mode;pattern recognition;correlation feature analysis