Estimation of Forearm Motion Based on EMG Using Quaternion Neural Network

Hafizzuddin Firdaus Bin Hashim; Takehiko Ogawa

doi:10.20965/jaciii.2022.p0269

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JACIII Vol.26 No.3 pp. 269-278

doi: 10.20965/jaciii.2022.p0269

(2022)

Paper:

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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:

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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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] K. Kanatani, “Understanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics,” CRC Press, 2015.

[2] [2] J. Vince, “Geometric Algebra for Computer Graphics,” Springer, 2010.

[3] [3] P. Corke, “Robotics, Vision and Control,” Springer, 2011.

[4] [4] B. Wie, H. Weiss, and A. Arapostathis, “Quaternion Feedback Regulator for Spacecraft Eigenaxis Rotations,” J. of Guidance Control and Dynamics, Vol.12, No.3, pp. 375-380, 1989.

[5] [5] R. Kristiansen and P. J. Nicklasson, “Satellite Attitude Control by Quaternion-Based Backstepping,” Proc. of the American Control Conf., pp. 907-911, 2005.

[6] [6] T. Nitta, “An extension of the back-propagation algorithm to quaternions,” Proc. of Int. Conf. on Neural Information Processing, Vol.1, pp. 247-250, 1996.

[7] [7] P. Arena, R. Caponetto, L. Fortuna, G. Muscato, and M. G. Xibilia, “Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E79A, No.9, pp. 1682-1688, 1996.

[8] [8] S. Buchholz and G. Sommer, “Introduction to neural computation in clifford algebra, and clifford algebra multilayer perceptrons,” G. Sommer (Ed.), “Geometric Computing with Clifford Algebras,” Chapters 12 and 13, Springer, 2001.

[9] [9] T. Nitta, “A Solution to the 4-bit Parity Problem with a Single Quaternary Neuron,” Neural Information Processing – Letters and Reviews, Vol.5, No.2, pp. 33-39, 2004.

[10] [10] M. Yoshida, Y. Kuroe, and T. Mori, “A Model of Hopfield-Type Quaternion Neural Networks and Its Energy Function,” Proc. of Int. Conf. on Neural Information Processing, pp. 110-115, 2004.

[11] [11] N. Matsui, T. Isokawa, H. Kusamichi, F. Peper, and H. Nishimura, “Quaternion neural network with geometrical operators,” J. of Intelligent and Fuzzy Systems, Vol.15, No.3-4, pp. 149-164, 2004.

[12] [12] L. Luo, H. Feng, and L. Ding, “Color Image Compression Based on Quaternion Neural Network Principal Component Analysis,” Proc. of Int. Conf. on Multimedia Technology, 2010.

[13] [13] Y. Xia, C. Jahanchahi, T. Nitta, and D. P. Mandic, “Performance Bounds of Quaternion Estimators,” IEEE Trans. on Neural Networks and Learning Systems, Vol.26, No.12, pp. 3287-3292, 2015.

[14] [14] H. Firdaus and T. Ogawa, “Application of Quaternion Neural Network to EMG-Based Estimation of Forearm Motion,” Proc. of the 2017 Int. Conf. on Artificial Intelligence, pp. 317-318, 2017.

[15] [15] M. F. Kelly, P. A. Parker, and R. N. Scott, “The Application of Neural Networks to Myoelectric Signal Analysis: A Preliminary Study,” IEEE Trans. on BME. Vol.37, No.3, pp. 221-230, 1990.

[16] [16] M. Hassan, A. Rahaman, F. Shuvo, A. S. Ovi, and M. Rahman, “Human Hand Gesture Detection Based on EMG Signal Using ANN,” Proc. of 3rd Int. Conf. on Informatics, Electronics & Vision, 2014.

[17] [17] T. Tsuji, O. Fukuda, M. Kaneko, and K. Ito, “Pattern classification of time-series EMG signals using neural networks,” Int. J. Adapt. Control Signal Process, Vol.14, pp. 829-848, 2000.

[18] [18] A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard, and Y. Laurillau, “EMG Feature Evaluation for Improving Myoelectric Pattern Recognition Robustness,” Expert Systems with Applications, Vol.40, pp. 4832-4840, 2013.

[19] [19] R. Drake, A. W. Vogl, and A. W. M. Mitchell, “Gray’s Anatomy for Students,” (Trans: K. Shiota and K. Akita), Elsevier Japan, 2016 (in Japanese).

[20] [20] V. M. Zatsiorsky, “Kinematics of Human Motion,” Human Kinetics, 1998.

[21] [21] M. Makikawa and M. Yoshida, “Biomechanics of Human Motion – Its Hardware and Software,” Corona Publishing, 2008 (in Japanese).