Towards improved myoelectric prosthesis control: High density surface EMG recording after targeted muscle reinnervation

Zhou Ping*, Madeleine M. Lowery, Julius P.A. Dewald, Todd A. Kuiken

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

The control of shoulder disarticulation prostheses remains a challenging problem. Recently, a novel method, using targeted muscle reinnervation to develop additional myoelectric control sites, has Improved the control of myoelectric upper limb prosthesis In a patient with bilateral amputations at the shoulder disarticulation level [1]. Encouraged by this achievement, we recorded high density surface electromyogram (EMG) signals from the patient's reinnervated muscles as he actuated a variety of different movements. We believe that the reinnervated muscle activation patterns revealed by the high density EMG recording have the potential to further improve the myoelectric prosthesis control. As the Initial stage of the study, this paper presents the experiment of high density surface EMG recording, the preliminary EMG data analysis and the framework for future investigation with advanced feature extraction and pattern recognition techniques.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Pages4064-4067
Number of pages4
StatePublished - 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/TerritoryChina
CityShanghai
Period9/1/059/4/05

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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