Entropy of surface EMG reflects object weight in grasp-and-lift task

Yuqi Li, Beth Jelfs, Rosa H.M. Chan

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

1 Citation (Scopus)

Abstract

Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.

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 (IEEE)
Pages2530-2533
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - 13 Sep 2017
Externally publishedYes
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Publication series

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

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/TerritoryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17

Bibliographical note

Funding Information:
*This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU110813].

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

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

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