Control Method Based on EMG for Power Assist Glove Using Self-Organizing Maps
Daisuke Sasaki*, Toshiro Noritsugu**, Masahiro Takaiwa*,
and Hidekazu Konishi***
*Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushimanaka, Kita-ku, Okayama 700-8530, Japan
**Tsuyama National College of Technology, 624-1 Numa, Tsuyama 708-8509, Japan
***Furuno Electric Co., Ltd., 9-52 Ashihara-cho, Nishinomiya 662-8580, Japan
In this study, electromyography is applied to a subject’s forearm to discriminate between intended finger and wrist motions and the resulting signals are used to control a power assist glove. This glove is designed to assist with grasping. The fingers and wrist are moved when the corresponding muscles of the forearm contract simultaneously. Therefore, the intention to move either a finger or a wrist must be discriminated to enable assisting a finger movement by this glove. The introduction of Self-Organizing Maps (SOM) improves the ability to discriminate between these motions. In this study, the effectiveness of a discrimination method using SOM is verified experimentally.
-  Y. Hasegawa, Y. Mikami, K. Watanabe, Z. Firouzimehr, and Y. Sankai, “Wearable Handing Support System for Paralyzed Patient,” Proc. of 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 741-746, 2008.
-  K. Tadano, M. Akai, K. Kadota, and K. Kawashima, “Development of Grip Amplified Glove using Bi-articular Mechanism with Pneumatic Artificial Rubber Muscle,” Proc. of 2010 IEEE Int. Conf. on Robotics and Automation, pp. 2363-2368, 2010.
-  Y. Kadowaki, T. Noritsugu, M. Takaiwa, D. Sasaki, and M. Kato, “Development of Soft Power-Assist Glove and Control Based on Human Intent,” J. of Robotics and Mechatronics, Vol.23, No.2, pp. 281-291, 2011.
-  K. Kuribayashi, K. Okimura, and T. Taniguchi, “A discrimination system using neural network for EMG-controlled prostheses,” Proc. of IEEE Int.Workshop on Robot and Human Communication, pp. 63-68, 1992.
-  T. Ando, J. Okamoto, and M. G. Fujie, “Micro Macro Neural Network to Recognize Rollover Movement,” Advanced Robotics, Vol.25, No.1-2, pp. 253-271, 2011.
-  T. Ando, J. Okamoto, M. Takahashi, and M. G. Fujie, “Response Evaluation of Rollover Recognition in Myoelectric Controlled Orthosis Using Pneumatic Rubber Muscle for Cancer Bone Metastasis Patients,” J. of Robotics and Mechatronics, Vol.23, No.2, pp. 302-309, 2011.
-  H. Konishi, T. Noritsugu, M. Takaiwa, and D. Sasaki, “Control Method of Power Assist Glove Based on Human Intention Measured by EMG,” Trans. of the Society of Instrument and Control Engineers, Vol.49, No.1, pp. 59-65, 2013. (in Japanese)
-  T. Kohonen, “The self-organizing map,” Proc. of the IEEE, Vol.78, No.9, pp. 1464-1480, 1990.
-  T. Yamakawa and K. Horio, “Self-Organizing Relationship (SOR) Network,” IEICE Tran. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E82-A, No.8, pp. 1674-1677, 1999.
-  H. D. Jin, K. S. Leung, M. L. Wong, and Z. B. Xu, “An Efficient Self-Organizing Map Designed by Genetic Algorithms for the Traveling Salesman Problem,” IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.33, No.6, pp. 877-888, 2003.
-  M. Milosevic, K. M. V. McConville, E. Sejdic, K. Masani, M. J. Kyan, and M. R. Popovic, “Visualization of Trunk Muscle Synergies During Sitting Perturbations Using Self-Organizing Maps (SOM),” IEEE Trans. on Biomedical Eng., Vol.59, No.9, pp. 2516-2523, 2012.
-  C. S. Pattichis, C. N. Schizas, and L. T. Middleton, “Neural Network Models in EMG Diagnosis,” IEEE Trans. on Biomedical Eng., Vol.42, No.5, pp. 486-496, 1995.
-  T. Ando, Y. Kojima, M. Seki, K. Kawamura, M. Nihei, H. Sato, Y. Tatsumi, Y. Ohno, T. Inoue, and M. G. Fujie, “An Electric Powered Wheelchair System using Interactive Learning for Child with Severe Cerebral Palsy,” J. of the Robotics Society of Japan, Vol.30, No.9, pp. 51-58, 2012. (in Japanese)
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