@inproceedings{6b5a7067415f4fdab3791670927018c6,
title = "Estimation of Grasp States in Prosthetic Hands using Deep Learning",
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.",
keywords = "deep learning, myoelectric hand, neural networks, prosthesis, prosthetic hand, recognition, state estimation",
author = "Victor Parque and Tomoyuki Miyashita",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 ; Conference date: 13-07-2020 Through 17-07-2020",
year = "2020",
month = jul,
doi = "10.1109/COMPSAC48688.2020.00-79",
language = "English",
series = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1285--1289",
editor = "Chan, {W. K.} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, {Sheikh Iqbal}",
booktitle = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",
}