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EMG-EMG correlation analysis for human hand movements

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a novel electromyogram (EMG)-EMG correlation analysis method is proposed to identify human hand movements. Mutual information (MI) measure is employed to analyse the ordinal pattern of the surface EMG recordings. The MI measure is extracted from EMG signals and compared with other various sEMG features in the time and frequency domains. The comparative experimental results demonstrate that autoregressive coefficients (AR)+MI has a better performance than the single features and other multi-features. The multi-features combining the different features mostly have improved the recognition performance, and the MI provides important supplemental information to the hand movements. It is evident that the proposed correlation feature is essential to improve the recognition rate.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Pages38-42
Number of pages5
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore
Duration: 16 Apr 201319 Apr 2013

Publication series

NameProceedings of the 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Conference

Conference2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, RiiSS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Country/TerritorySingapore
CitySingapore
Period16/04/1319/04/13

Keywords

  • EMG-EMG Correlation
  • Human Hand Movements
  • Mutual Information

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