著者

平岩 明 内田 典佳 曾根原 登 下原 勝憲

雑誌

情報処理学会研究報告ヒューマンコンピュータインタラクション(HCI)

巻号頁・発行日

vol.1992, no.31(1992-HI-042), pp.97-104, 1992-05-11

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The cybernetic interface through which users can communicate with computers "as we may think" is the dream of human-computer interactions. Aiming at interfaces where machines adapt themselves to users' intention instead of users' adaptation to machines we have been applying neural networks to realize electromyographic (EMG)-controlled prosthetic members-a historical heritage of the cybernetics. This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and simulations it is demonstrated that recognition of not only finger movement and torque but also joint angles in dynamic finger movement based on EMG patterns can be successfully accomplished.

著者

平岩 明 内田 典佳 下原 勝憲 曽根原 登

出版者

The Society of Instrument and Control Engineers

雑誌

計測自動制御学会論文集 (ISSN:04534654)

巻号頁・発行日

vol.30, no.2, pp.216-224, 1994-02-28 (Released:2009-03-27)

参考文献数

19

被引用文献数

8 15

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The cybernetic interface through which users can communicate with computers “as we may think” is the dream of human-computer interactions. Aiming at interfaces where machines adapt themselves to users' intention instead of users' adaptation to machines, we have been applying a neural network to realize electromyographic (EMG)-controlled slave hand. This paper proposes that EMG patterns can be analyzed and classified by a neural network. Through experiments and simulations, it is shown that recognition of finger movement and joint angles in dynamic finger movement can be successfully accomplished.A 3-layred back-propagation network is used for finger action recognition from 1 or 2ch surface EMG. In the case of static fingers' motions recognition, 5 categories were classified by the neural network and the recognition rate was 86%. In the case of joint angles estimation in continuous finger motion, the root mean square error was under 25 degrees for 5 fingers 10 joints angles' estimations.Cyber Finger with 5 fingers 10 joint angles was realized to be controlled by 2ch surface EMG. The slave hand was controlled smoothly and voluntarily by a neural network.