Top > JRM > Stiffness Teaching and Motion Assist System Using Functional E ...

single-rb.php

JRM Vol.16 No.5 pp. 446-455
doi: 10.20965/jrm.2004.p0446
(2004)

Paper:

Views over last 60 days: 448

Stiffness Teaching and Motion Assist System Using Functional Electrical Stimulation and Electromyogram Signals

Masaaki Uechi, Yutaka Naito, Duk Shin,
Makoto Sato, and Yasuharu Koike

Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan

Received:
February 25, 2004
Accepted:
May 20, 2004
Published:
October 20, 2004
Creative Commons License
Keywords:
FES, EMG, stiffness, motion assist, teaching
Abstract
In skilful tasks and sports, not only movement but also joint stiffness and the force are important. It is easy to watch and replicate the movement, but not to replicate the stiffness and the force. Muscle tensions cause our movement, which can be measured by EMG. Using the signals from EMG, we develop the technique to estimate the joint torque, joint stiffness, and equilibrium posture. So, while watching the hand movement, we can also feel the force and the joint torque are used. In this paper, we propose a motion assist system (MAS) that uses the pieces of internal information that are joint stiffness and joint torque based on muscle tensions. The experimental results show that the joint torque and hand stiffness were transmitted precisely using functional electrical stimulation (FES). This system will be useful not only for learning a skill, but also for supporting elder persons.
Cite this article as:
M. Uechi, Y. Naito, D. Shin, M. Sato, and Y. Koike, “Stiffness Teaching and Motion Assist System Using Functional Electrical Stimulation and Electromyogram Signals,” J. Robot. Mechatron., Vol.16 No.5, pp. 446-455, 2004.
Data files:
References
  1. [1] T. Koyama, K. Yamafuji, and T. Tanaka, “Development and Motion Control of a Wearable-Human Assisting Robot for Nursing Use,” JSME International Journal Series C, Vol.67, No.651, pp. 3679-3684, 2000.
  2. [2] S. Lee, and Y. Sankai, “Power Assist Control for Walking Aid with HAL-3 Based on EMG and Impedance Adjustment around Knee Joint,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2002), pp. 1499-1504 (CD-ROM), 2002.
  3. [3] T. Noristugu, T. Tanaka, and T. Yamanaka, “Application of Rubber Artificial Muscle Manipulator as a Rehabilitation Robot,” Proceeding of IEEE International Workshop on Robot and Human Communication, pp. 112-117, 1996.
  4. [4] H. Kobayashi, J. Aoki, H. Hosono, T. Matsushita, Y. Ishida, K. Kikuchi, and M. Koseki, “Concept of Wear-type Muscular Support Apparatus (Muscle Suit),” Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pp. 3236-3241, 2002.
  5. [5] M. F. Kelly, P. A. Parker, and R. N. Scott, “The Application of Neural Networks to Myoelectric Signal Analysis: A preliminary study,” IEEE Trans. Biomedical Eng., Vol.37, No.3, pp. 221-230, 1990.
  6. [6] O. Fukuda, T. Tsuji, and M. Kaneko, “A Human Supporting Manipulator Based on Manual Control Using EMG Signals,” Journal of the Robotics Society of Japan, Vol.18, No.3, pp. 387-394, 2000.
  7. [7] A. Hiraiwa, N. Uchida, K. Shimohara, and N. Sonehara, “Neural Network Recognition of Electro-Encephalogram Patterns Preceding Voluntary Movements,” Electronics and Communications in Japan, Part 3, Vol.80, No.7, pp. 65-73, 1997.
  8. [8] Y. Koike, O. Shimada, T. Shin, and M. Sato, “Direct Arm Impedance Estimation Using Surface EMG Signals,” Neuroscience 2002, Orlando Florida USA, 2002.
  9. [9] R. Davoodi, and B. J. Andrews, “Optimal control of FES-assisted standing up in paraplegia using genetic algorithms,” Med. Eng. Phys., Vol.21, pp. 609-617, 1999.
  10. [10] T. Watanabe, H. Iibuchi, K. Kurosawa, and N. Hoshimiya, “Wrist joint control by multichannel closed-loop FES system,” Electronics and Communications in Japan, Part 2, J85-D II-2, pp. 319-328, 2002.
  11. [11] K. Kubo, K. Fujita, N. Itakura, Y. Iguchi, and H. Minamitani, “Disturbance Suppression on Two Joints Controller Using FES with Antagonist Muscle Stiffness Control,” Electronics and Communications in Japan, Part 2, J74-DII-2, pp. 274-281, 1991.
  12. [12] T. Iwami, H. Miura, K. Hasegawa, A. Nakayama, G. Obinata, K. Miyawaki, and Y. Yanagihara, “A New Bilateral Teleoperator with Force Reflection using Functional Electrical Stimulation,” Journal of the Robotics Society of Japan, Vol.20, No.8, pp. 66-73, 2002.
  13. [13] T. Tsuji, T. Shimazaki, and M. Kaneko, “Analysis of Human Perception Ability for Robot Impedance,” Journal of the Robotics Society of Japan, Vol.20, No.2, pp. 58-64, 2002.
  14. [14] L. A. Jones, and I. W. Hunter, “A perceptual analysis of viscosity,” Experimental Brain Research, Vol.94, No.2, pp. 343-351, 1993.
  15. [15] N. Yoshida, K. Domen, Y. Koike, and M. Kawato, “A method for estimating torque-vector directions of shoulder muscles using surface EMGs,” Biological Cybernetics, Vol.86, pp. 167-177, 2002.

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

Copyright© 2004 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024