Estimation of Grasp States in Prosthetic Hands using Deep Learning

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

Abstract

The estimation of grasp states in myoelectric prosthetic hands is relevant for ergonomic interfacing, control and rehabilitation initiatives. In this paper we evaluate the possibility to infer the grasp state of a prosthetic hand from RGB frames by using well-known deep learning architectures in testing scenarios involving variations of brightness, contrast and flips. Our results show the feasibility, the attractive accuracy and efficiency to estimate prosthetic hand poses with a GoogLeNet-based deep architecture using relatively few training frames.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
EditorsW. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1285-1289
Number of pages5
ISBN (Electronic)9781728173030
DOIs
Publication statusPublished - 2020 Jul
Event44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain
Duration: 2020 Jul 132020 Jul 17

Publication series

NameProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

Conference

Conference44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
CountrySpain
CityVirtual, Madrid
Period20/7/1320/7/17

Keywords

  • deep learning
  • myoelectric hand
  • neural networks
  • prosthesis
  • prosthetic hand
  • recognition
  • state estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software
  • Education

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