Top > JRM > A Power Assist Device Based on Joint Equilibrium Point Estimat ...

single-rb.php

JRM Vol.24 No.1 pp. 205-218
doi: 10.20965/jrm.2012.p0205
(2012)

Paper:

Views over last 60 days: 1,234

A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals

Toshihiro Kawase*, Hiroyuki Kambara*,**, and Yasuharu Koike*,**

*Tokyo Institute of Technology, R2-15, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan

**JST CREST, 4-1-8 Honmachi, Kawaguchi-shi 332-0012, Japan

Received:
April 28, 2011
Accepted:
October 6, 2011
Published:
February 20, 2012
Creative Commons License
Keywords:
exoskeleton, electromyography, equilibrium point
Abstract
In some researches about power assist devices, surface ElectroMyoGraphy (EMG) signals are used to estimate user intentions to move their limbs. These conventional methods mainly focus on estimation of joint torque. However, the devices based on torque estimation are inclined to cause the vibration of users’ posture originating from the waviness of the EMG signals. Focusing on estimation of states related to the joint angle may improve the performance of the power assist devices. This paper proposes a new method that estimates user joint equilibrium point and stiffness separately from the EMG and that amplifies the stiffness while tuning the device joints according to user equilibrium points. To evaluate the method, we constructed a power assist system for the wrist and compared the method with a method based on simple torque estimation during posture maintenance tasks. Our results showed that the proposed method offers a more stable operation at the same assist ratio and proved the effectiveness of the method.
Cite this article as:
T. Kawase, H. Kambara, and Y. Koike, “A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals,” J. Robot. Mechatron., Vol.24 No.1, pp. 205-218, 2012.
Data files:
References
  1. [1] E. E. Cavallaro, J. Rosen, J. C. Perry, and S. Burns, “Real-TimeMyoprocessors for a Neural Controlled Powered Exoskeleton Arm,” IEEE Trans. on Biomedical Engineering, Vol.53, No.11, pp. 2387-2396, 2006.
  2. [2] C. Fleischer and G. Hommel, “A Human-Exoskeleton Interface Utilizing Electromyography,” IEEE Trans. on Robotics, Vol.24, No.4, pp. 872-882, 2008.
  3. [3] E. Guizzo and H. Goldstein, “The Rise of the Body Bots,” IEEE Spectrum, Vol.42, No.5, pp. 42-48, 2005.
  4. [4] T. Hayashi, H. Kawamoto, and Y. Sankai, “Control Method of Robot Suit HAL Working as Operator’s Muscle Using Biological and Dynamical Information,” In Proc. of 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2005), pp. 3063-3068, 2005.
  5. [5] H. Kazerooni and R. Steger, “The Berkeley Lower Extremity Exoskeleton,” ASME J. of Dynamic Systems, Measurement and Control, Vol.128, No.1, pp. 14-25, 2006.
  6. [6] K. Kiguchi, S. Kariya, K. Watanabe, K. Izumi, and T. Fukuda, “An Exoskeletal Robot for Human Elbow Motion Support – Sensor Fusion, Adaptation, and Control,” IEEE Trans. on Systems Man and Cybernetics Part B – Cybernetics, Vol.31, No.3, pp. 353-361, 2001.
  7. [7] K. Kiguchi, T. Tanaka, and T. Fukuda, “Neuro-Fuzzy Control of a Robotic Exoskeleton with EMG Signals,” IEEE Trans. on Fuzzy Systems, Vol.12, No.4, pp. 481-490, 2004.
  8. [8] J. Rosen, M. Brand, M. B. Fuchs, and M. Arcan, “A Myosignal-Based Powered Exoskeleton System,” IEEE Trans. on SystemsMan and Cybernetics Part A – Systems and Humans, Vol.31, No.3, pp. 210-222, 2001.
  9. [9] S. Toyama and G. Yamamoto, “Development of Wearable-Agri-Robot – Mechanism for Agricultural Work –,” In Proc. of 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2009), pp. 5801-5806, 2009.
  10. [10] P. K. Artemiadis and K. J. Kyriakopoulos, “EMG-Based Position and Force Estimates in Coupled Human-Robot Systems: Towards EMG-Controlled Exoskeletons,” O. Khatib, V. Kumar, and D. Rus (Eds.), Experimental Robotics, Springer, pp. 241-250, 2009.
  11. [11] F. Lacquaniti and C. Maioli, “The Role of Preparation in Tuning Anticipatory and Reflex Responses During Catching,” J. of Neuroscience, Vol.9, No.1, pp. 134-148, 1989.
  12. [12] D. Shin, J. Kim, and Y. Koike, “A Myokinetic Arm Model for Estimating Joint Torque and Stiffness From EMGSignals During Maintained Posture,” J. of Neurophysiology, Vol.101, No.1, pp. 387-401, 2009.
  13. [13] Y. Koike and M. Kawato, “Estimation of Dynamic Joint Torques and Trajectory Formation from Surface Electromyography Signals Using a Neural Network Model,” Biological Cybernetics, Vol.73, No.4, pp. 291-300, 1995.
  14. [14] N. Hogan, “Adaptive-Control of Mechanical Impedance by Coactivation of Antagonist Muscles,” IEEE Trans. on Automatic Control, Vol.29, No.8, pp. 681-690, 1984.
  15. [15] Y. Koike, J. Kim, and D. Shin, “Role of Stiffness in Weight Perception,” Japanese Psychological Research, Vol.48, No.3, pp. 174-187, 2006.
  16. [16] J. Izawa, T. Shimizu, T. Aodai, T. Kondo, H. Gomi, S. Toyama, and K. Ito, “MR Compatible Manipulandum with Ultrasonic Motor for fMRI Studies,” In Proc. of 2006 IEEE Int. Conf. on Robotics and Automation (ICRA 2006), pp. 3850-3854, 2006.
  17. [17] R. Osu, N. Kamimura, H. Iwasaki, E. Nakano, C. M. Harris, Y. Wada, and M. Kawato, “Optimal Impedance Control for Task Achievement in the Presence of Signal-Dependent Noise,” J. of Neurophysiology, Vol.92, No.2, 1199-1215, 2004.
  18. [18] P. A. Cook, “Nonlinear Dynamical Systems,” Prentice-Hall Int., UK, 1986.
  19. [19] K. Ito and T. Tsuji, “Bilinear Characteristics of Musculo-Skeletal Motor Systems and Their Application to Prosthesis Control,” Trans. of the Institute of Electrical Engineers of Japan, Vol.C-105, No.10, pp. 201-208, 1985. (in Japanese)

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

Copyright© 2012 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 Sep. 09, 2024