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


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

September 14, 2017
January 28, 2022
May 20, 2022
quaternion neural networks, surface electromyography, forearm motion

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:
H. Hashim and T. 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.
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