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ID 48207
file
creator
Eto, Shintaro
Nakagaki, Kosuke
Shimada, Kyohei
Nakamura, Go
Masuda, Akito
Chin, Takaaki
abstract
Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.
description
This work was partially supported by JSPS KAKENHI Grants-in-Aid for Scientific Research C Number 26462242.
journal title
Science Robotics
volume
Volume 4
issue
Issue 31
start page
eaaw6339
date of issued
2019-06-26
publisher
American Association for the Advancement of Science
issn
2470-9476
publisher doi
language
eng
nii type
Journal Article
HU type
Journal Articles
DCMI type
text
format
application/pdf
text version
author
rights
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science.
This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Robotics on Vol. 4, Issue 31, 26 Jun 2019, DOI: 10.1126/scirobotics.aaw6339.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認、ご利用ください。
relation url
department
Graduate School of Engineering