Reach-to-grasp motions: Towards a dynamic classification approach for upper-limp prosthesis

I. Batzianoulis, A. M. Simon, L. Hargrove, A. Billard

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

Abstract

During reach-to-grasp motions, the Electromyo-graphic (EMG) activity of the arm varies depending on motion stage. The variability of the EMG signals results in low classification accuracy during the reaching phase, delaying the activation of the prosthesis. To increase the efficiency of the pattern-recognition system, we investigate the muscle activity of four individuals with below-elbow amputation performing reach-to-grasp motions and segment the arm-motion into three phases with respect to the extension of the arm. Furthermore, we model the dynamic muscle contractions of each class with Gaussian distributions over the different phases and the overall motion. We quantify of the overlap among the classes with the Hellinger distance and notice larger values and, thus, smaller overlaps among the classes with the segmentation to motion phases. A Linear Discriminant Analysis classifier with phase segmentation affects positively the classification accuracy by 6 - 10% on average.

Original languageEnglish (US)
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages287-290
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - May 16 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period3/20/193/23/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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    Batzianoulis, I., Simon, A. M., Hargrove, L., & Billard, A. (2019). Reach-to-grasp motions: Towards a dynamic classification approach for upper-limp prosthesis. In 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 (pp. 287-290). [8717110] (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March). IEEE Computer Society. https://doi.org/10.1109/NER.2019.8717110