A Prosthetic Hand Control Based on Nonstationary EMG at the Start of Movement

Masakatsu Tsukamoto; Toshiyuki Kondo; Koji Ito

doi:10.20965/jrm.2007.p0381

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JRM Vol.19 No.4 pp. 381-387

doi: 10.20965/jrm.2007.p0381

(2007)

Paper:

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A Prosthetic Hand Control Based on Nonstationary EMG at the Start of Movement

Masakatsu Tsukamoto^, Toshiyuki Kondo^, and Koji Ito^

^*NTT DoCoMo R&D Center, 3-5 Hikarino-oka, Yokosuka-shi, Kanagawa 239-8536, Japan

^**Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

^***Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259-G3-50 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:

January 11, 2007

Accepted:

April 12, 2007

Published:

August 20, 2007

Keywords:

nonstationary electromyogram, prosthetic hand, neural network, EMG

Abstract

We propose using a multiple neural network to determine the movement intended by an amputee from electromyogram (EMG) signals. Most previous approaches to the discrimination of movement using EMG signals have required EMG data with a relative long period exceeding 200 ms. Our approach enables the amputee’s intended movement to be determined from among six limb functions based on EMG signals using an initial rise zone 70 ms long. Experiments with four subjects and four electrode locations demonstrated that our proposal determines six forearm movements at a discrimination rate exceeding than 90%.

Cite this article as:

M. Tsukamoto, T. Kondo, and K. Ito, “A Prosthetic Hand Control Based on Nonstationary EMG at the Start of Movement,” J. Robot. Mechatron., Vol.19 No.4, pp. 381-387, 2007.

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References

[1] T. Chin and J. Oba, “Acquiring ADL in Trans-radial Amputee using Myoelectric Prosthesis,” JRSJ, Vol.23, pp. 773-338, 2005.
[2] K. Akazawa, H. Takizawa, Y. Hayashi, and K. Fujii, “Development of Control System and Myoelectric Signal Processor for Bio-Mimetic Prosthetic Hand,” SOBIM, Vol.9, pp. 43-53, 1987.
[3] S.Morita, K. Shibata, X.-Z. Zheng, and K. Ito, “Human-EMG Prosthetic Hand Interface using Neural Network,” Technical Report of IEICE, MBE99-167, pp. 118-123, 2000.
[4] M. Ohga, M. Takeda, A. Matsuba, A. Koike, and T. Tuji, “Development of A Five-finger Prosthetic Hand Using Ultrasonic Motors Controlled by Two EMG Signals,” Journal of Robotics and Mechatronics, Vol.14-6, pp. 565-572, 2002.
[5] T. Tsuji, K. Ito, and M. Nagamachi, “A Limb-Function Discrimination Method Using EMG Signals for the Control of Multifunctional Powered Prostheses,” IEICE Transactions, Vol.J70-D-1, pp. 207-215, 1987.
[6] B. Hudgins, P. Parker, and R. N. Scott, “A New Strategy for Multifunction Myoelectric Control,” IEEE Trans. Biomed. Eng., Vol.40-1, pp. 82-94, 1993.
[7] F. H. Y. Chan, Y.-S. Yang, F. K. Lam, Y.-T. Zhang, and P. A. Parker, “Fuzzy EMG Classification for Prosthesis Control,” IEEE Trans. Rehab. Eng., Vol.8-3, pp. 305-311, 2000.
[8] T. Tsuji, H. Ichinobe, K. Ito, M. Nagamachi, “Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy,” SICE Trans., Vol.29-10, pp. 1213-1220, 1993.
[9] D. Nishikawa, W. Yu, H. Yokoi, and Y. Kakazu, “On-Line Supervising Mechanism for Learning Data in Surface Electromyogram Motion Classifiers,” IEICE Trans., Vol.J84-D-II-12, pp. 2634-2643, 2001.
[10] D. Nishikawa, W. Yu, H. Yokoi, and Y. Kakazu, “On-Line Learning Method for EMG Prosthetic Hand Controlling,” IEICE Trans., Vol.J82-D-II-9, pp. 1510-1519, 1999.
[11] J. U. Chu, I. Moon, S. K. Kim, and M. Mun, “Control of Multifunction Myoelectric Hand using a Real-Time EMG Pattern Recognition,” Proc. of IEEE/RSJ Int’l Conf. on Robots and Systems (IROS2005).
[12] O. Fukuda, N. Bu, and T. Tsuji, “Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals,” SICE Trans., Vol.40-11, pp. 1124-1131, 2004.
[13] T. Tuji, D. Mori, and K. Ito, “Motion Discrimination Method from EMG Signals using Statistically Structured Neural Networks,” Transations of the IEEJ, IEEJ Trans,, 112-C-8, pp. 465-473, 1992.
[14] O. Fukuda, T. Tuji, and M. Kaneko, “Pattern Classification of EMG Signals Using Neural Networks during a Series of Motion,” IEEJ Trans, Vol.117-C, No.10, pp. 1490-1497, 1997.
[15] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, “Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal,” Critical Rev. in Bio. Eng., 30(4-6), pp. 459-485, 2002.

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

[1] [1] T. Chin and J. Oba, “Acquiring ADL in Trans-radial Amputee using Myoelectric Prosthesis,” JRSJ, Vol.23, pp. 773-338, 2005.

[2] [2] K. Akazawa, H. Takizawa, Y. Hayashi, and K. Fujii, “Development of Control System and Myoelectric Signal Processor for Bio-Mimetic Prosthetic Hand,” SOBIM, Vol.9, pp. 43-53, 1987.

[3] [3] S.Morita, K. Shibata, X.-Z. Zheng, and K. Ito, “Human-EMG Prosthetic Hand Interface using Neural Network,” Technical Report of IEICE, MBE99-167, pp. 118-123, 2000.

[4] [4] M. Ohga, M. Takeda, A. Matsuba, A. Koike, and T. Tuji, “Development of A Five-finger Prosthetic Hand Using Ultrasonic Motors Controlled by Two EMG Signals,” Journal of Robotics and Mechatronics, Vol.14-6, pp. 565-572, 2002.

[5] [5] T. Tsuji, K. Ito, and M. Nagamachi, “A Limb-Function Discrimination Method Using EMG Signals for the Control of Multifunctional Powered Prostheses,” IEICE Transactions, Vol.J70-D-1, pp. 207-215, 1987.

[6] [6] B. Hudgins, P. Parker, and R. N. Scott, “A New Strategy for Multifunction Myoelectric Control,” IEEE Trans. Biomed. Eng., Vol.40-1, pp. 82-94, 1993.

[7] [7] F. H. Y. Chan, Y.-S. Yang, F. K. Lam, Y.-T. Zhang, and P. A. Parker, “Fuzzy EMG Classification for Prosthesis Control,” IEEE Trans. Rehab. Eng., Vol.8-3, pp. 305-311, 2000.

[8] [8] T. Tsuji, H. Ichinobe, K. Ito, M. Nagamachi, “Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy,” SICE Trans., Vol.29-10, pp. 1213-1220, 1993.

[9] [9] D. Nishikawa, W. Yu, H. Yokoi, and Y. Kakazu, “On-Line Supervising Mechanism for Learning Data in Surface Electromyogram Motion Classifiers,” IEICE Trans., Vol.J84-D-II-12, pp. 2634-2643, 2001.

[10] [10] D. Nishikawa, W. Yu, H. Yokoi, and Y. Kakazu, “On-Line Learning Method for EMG Prosthetic Hand Controlling,” IEICE Trans., Vol.J82-D-II-9, pp. 1510-1519, 1999.

[11] [11] J. U. Chu, I. Moon, S. K. Kim, and M. Mun, “Control of Multifunction Myoelectric Hand using a Real-Time EMG Pattern Recognition,” Proc. of IEEE/RSJ Int’l Conf. on Robots and Systems (IROS2005).

[12] [12] O. Fukuda, N. Bu, and T. Tsuji, “Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals,” SICE Trans., Vol.40-11, pp. 1124-1131, 2004.

[13] [13] T. Tuji, D. Mori, and K. Ito, “Motion Discrimination Method from EMG Signals using Statistically Structured Neural Networks,” Transations of the IEEJ, IEEJ Trans,, 112-C-8, pp. 465-473, 1992.

[14] [14] O. Fukuda, T. Tuji, and M. Kaneko, “Pattern Classification of EMG Signals Using Neural Networks during a Series of Motion,” IEEJ Trans, Vol.117-C, No.10, pp. 1490-1497, 1997.

[15] [15] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, “Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal,” Critical Rev. in Bio. Eng., 30(4-6), pp. 459-485, 2002.

A Prosthetic Hand Control Based on Nonstationary EMG at the Start of Movement

Masakatsu Tsukamoto*, Toshiyuki Kondo**, and Koji Ito***

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Masakatsu Tsukamoto^, Toshiyuki Kondo^, and Koji Ito^