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JACIII Vol.26 No.3 pp. 269-278
doi: 10.20965/jaciii.2022.p0269
(2022)

Paper:

Estimation of Forearm Motion Based on EMG Using Quaternion Neural Network

Hafizzuddin Firdaus Bin Hashim and Takehiko Ogawa

Graduate School of Engineering, Takushoku University
815-1 Tatemachi, Hachioji, Tokyo 193-0985, Japan

Received:
September 14, 2017
Accepted:
January 28, 2022
Published:
May 20, 2022
Keywords:
quaternion neural networks, surface electromyography, forearm motion
Abstract

Quaternions are useful for representing data in three-dimensional space, and the quaternion neural network is effective for learning data in this context. On the other hand, estimating biological motion based on myopotential can be performed directly using electromyogram (EMG) signals as the computer interface. The trajectory of human forearm movement within the three-dimensional space can provide important information. In this study, the relationship between the myopotential of the upper arm muscles and the forearm motion was estimated and investigated using a quaternion neural network.

Cite this article as:
Hafizzuddin Firdaus Bin Hashim and Takehiko Ogawa, “Estimation of Forearm Motion Based on EMG Using Quaternion Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.3, pp. 269-278, 2022.
Data files:
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Last updated on Jul. 01, 2022