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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern

  • Kim, Deok-Hwan (Department of Electronic Engineering, Inha University) ;
  • Cho, Chi-Young (Department of Japanese Business, Busan University of Foreign Studies) ;
  • Ryu, Jaehwan (Department of Electronic Engineering, Inha University)
  • Received : 2013.01.17
  • Accepted : 2013.09.16
  • Published : 2014.02.01
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Abstract

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% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) 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.

Keywords

References

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