| Item type |
デフォルトアイテムタイプ_（フル）(1) |
| 公開日 |
2023-03-18 |
| タイトル |
|
|
タイトル |
A human-assisting manipulator teleoperated by EMG signals and arm motions |
|
言語 |
en |
| 作成者 |
Fukuda, Osamu
Tsuji, Toshio
Kaneko, Makoto
Otsuka, Akira
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
Copyright (c) 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Adaptation |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
electromyographic(EMG)signals |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
human-assisting manipulator |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
neural network |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
pattern discrimination |
| 主題 |
|
|
主題Scheme |
NDC |
|
主題 |
530 |
| 内容記述 |
|
|
内容記述 |
This paper proposes a human-assisting manipulator teleoperated by electromyographic (EMG) signals and arm motions. The proposed method can realize a new master-slave manipulator system that uses no mechanical master controller. A person whose forearm has been amputated can use this manipulator as a personal assistant for desktop work. The control system consists of a hand and wrist control part and an arm control part. The hand and wrist control part selects an active joint in the manipulator's end-effector and controls it based on EMG pattern discrimination. The arm control part measures the position of the operator's wrist joint or the amputated part using a three-dimensional position sensor, and the joint angles of the manipulator's arm, except for the end-effector part, are controlled according to this position, which, in turn, corresponds to the position of the manipulator's joint. These control parts enable the operator to control the manipulator intuitively. The distinctive feature of our system is to use a novel statistical neural network for EMG pattern discrimination. The system can adapt itself to changes of the EMG patterns according to the differences among individuals, different locations of the electrodes, and time variation caused by fatigue or sweat. Our experiments have shown that the developed system could learn and estimate the operator's intended motions with a high degree of accuracy using the EMG signals, and that the manipulator could be controlled smoothly. We also confirmed that our system could assist the amputee in performing desktop work. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
IEEE |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1109/TRA.2003.808873 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
http://dx.doi.org/10.1109/TRA.2003.808873 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
1042-296X |
| 開始ページ |
|
|
開始ページ |
210 |
| 書誌情報 |
IEEE Transactions on Robotics and Automation
IEEE Transactions on Robotics and Automation
巻 19,
号 2,
p. 210-222,
発行日 2003
|
| 旧ID |
14211 |
IEEE_TRA_19_2_210-222_2003.pdf (2.5 MB)
