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Recognition of Ten Upper Limb Movements Based on Surface Electromyography Signals

  • Zhifeng Qian
  • , Shunli Liu
  • , Juan Li
  • , Hao Guo
  • , Ben Huang
  • , Hongmiao Zhang*
  • , Lining Sun
  • *Corresponding author for this work

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

Abstract

Lots of patients suffer from lack of motion ability or even lost of limbs. Rehabilitation robot aims to assist them to regain some motion ability. We study the recognition of ten upper limb movements based on surface electromyography (sEMG) signals for future potential robotic arm control. After sEMG are collected and denoised, wavelet transform is used to construct the feature vector. Recognition rate among ten movements reaches 96.75% revealed by eight channel sEMG signals and 96.25% by two channels sEMG. This provides the basis for further real-time control of robotic arm.

Original languageEnglish
Title of host publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages466-471
Number of pages6
ISBN (Print)9781538604892
DOIs
StatePublished - 24 Aug 2018
Externally publishedYes
Event7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017 - Honolulu, United States
Duration: 31 Jul 20174 Aug 2017

Publication series

Name2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

Conference

Conference7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Country/TerritoryUnited States
CityHonolulu
Period31/07/174/08/17

Keywords

  • Fisher discriminant classifier
  • surface electromyography signals
  • ten upper limbs motion
  • wavelet transform

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