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