Estimation of Grasp States in Prosthetic Hands using Deep Learning

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
編集者W. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1285-1289
ページ数5
ISBN(電子版)9781728173030
DOI
出版ステータスPublished - 2020 7
イベント44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain
継続期間: 2020 7 132020 7 17

出版物シリーズ

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

Conference

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

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • 教育

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