Skip to main navigation Skip to search Skip to main content

Surface EMG signals determinism analysis based on recurrence plot for hand grasps

  • Gaoxiang Ouyang*
  • , Zhaojie Ju
  • , Honghai Liu
  • *Corresponding author for this work
  • University of Portsmouth

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

Abstract

This paper proposes determinism measure (DET) based on recurrence plot, which is capable of showing the recurrence property of a deterministic dynamical system, to evaluate the dynamical characteristics of the surface electromyogram (sEMG) during three different hand movements. In addition, the linear discriminant analysis (LDA) is applied to evaluate the performance of the above measures to identify these three hand grasp movements. The experimental result shows that the recognition rate, 96.7%, based on the combination of the linear and non-linear measures is much higher than that with only linear measures, and DET might be a potential tool to reveal the sEMG hidden characteristics of hand grasp movements.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • Determinsm
  • hand grasp
  • recurrence plot (RP)
  • surface electromyogram (sEMG)

Fingerprint

Dive into the research topics of 'Surface EMG signals determinism analysis based on recurrence plot for hand grasps'. Together they form a unique fingerprint.

Cite this