Importance of muscle selection for EMG signal analysis during upper limb rehabilitation of stroke patients

A. Costa, M. Itkonen, H. Yamasaki, F. S. Alnajjar, S. Shimoda

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

12 Citations (Scopus)

Abstract

Current work highlights the importance of muscle selection to evaluate paralysis and recovery level of stroke patients when comparing synergies of affected and non-affected side of the body. The proposed method allows the selection of important muscles that highly contribute to the specific movements according to the power and frequency distribution of the electromyographic signals.. Users participating performed steering-wheel-based therapy focused on upper limb rehabilitation. Final results show that with the appropriate muscles selection, it is possible to compute a Similarity Index between right and left arms (during symmetric motion) associated to the level of paralysis and potential recovery of a given subject.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2510-2513
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - Sep 13 2017
Externally publishedYes
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: Jul 11 2017Jul 15 2017

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period7/11/177/15/17

ASJC Scopus subject areas

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

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