Top > JRM > Spinal Information Processing and its Application to Motor Lea ...

single-rb.php

JRM Vol.17 No.6 pp. 617-627
doi: 10.20965/jrm.2005.p0617
(2005)

Paper:

Views over last 60 days: 399

Spinal Information Processing and its Application to Motor Learning Support

Mihoko Otake*,**, and Yoshihiko Nakamura***

*Division of Project Coordination, University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8568, Japan

**PRESTO program, Japan Science and Technology Agency

***Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
January 28, 2005
Accepted:
June 14, 2005
Published:
December 20, 2005
Creative Commons License
Keywords:
motor learning, muscle innervation, simulation, spinal cord, human behavior analysis
Abstract
We designed motor learning support for acquiring motor skills involving neural mechanisms. We should be able to acquire neural information by analyzing whole-body muscle data, because the nervous system controls the musculoskeletal system and lengths and forces information is fed back to the nervous system. Motor information is calculated by mapping motion-capture data on to a musculoskeletal human model. Neural information represents the set of motor information on the muscles innervated by the arbitrary spinal cord segment. Neural information processing is proposed which calculates correlation among the neural information. We demonstrate the effectiveness of our proposal by experimental results of “kesagiri.”
Cite this article as:
M. Otake and Y. Nakamura, “Spinal Information Processing and its Application to Motor Learning Support,” J. Robot. Mechatron., Vol.17 No.6, pp. 617-627, 2005.
Data files:
References
  1. [1] N. Yamazaki, and K. Hase, “Development of three-dimensional whole body musculoskeletal model for various motion analyses,” JSME International Journal, Series C, Vol.40, pp. 25-32, 1997.
  2. [2] T. Komura, and Y. Shinagawa, “Attaching physiological effects to motion-capture data,” in Proceedings of Graphics Interface, 2001, pp. 27-36.
  3. [3] D. G. Lloyd, and T. S. Buchanan, “Strategies of muscular support of varus and valgus isometric loads at the human knee,” Journal of Biomechanics, Vol.34, pp. 1257-1267, 2001.
  4. [4] Y. Nakamura et al., “Dynamic computation of musculo-skeletal human model based on efficient algorithm for closed kinematic chains,” in Proceedings of the 2nd International Symposium on Adaptive Motion of Animals and Machines, 2003.
  5. [5] D. Davy, and M. Audu, “A dynamic optimization technique for predicting muscle forces in the swing phase of gait,” Journal of Biomechanics, Vol.20, pp. 187-201, 1987.
  6. [6] M. Kaplan, and J. Heegaard, “Predictive algorithms for neuromuscular control of human locomotion,” Journal of Biomechanics, Vol.34, pp. 1077-1083, 2001.
  7. [7] S. L. Delp, and P. J. Loan, “A computational framework for simulating and analyzing human and animal movement,” IEEE Computing in Science and Engineering, Vol.2, pp. 46-55, 2000.
  8. [8] J. Rasmussen et al., “Anybody – a software system for ergonomic optimization,” in Fifth World Congress on Structural and Multidisciplinary Optimization, 2003.
  9. [9] S. Sakurai et al., “Muscle activity and performance accuracy of the smash stroke in badminton with reference to skill and practice,” J. Sports Science, Vol.18, pp. 1-14, 2000.
  10. [10] E. R. Kandel, J. H. Schwartz, and T. M. Jessell, “Principles of neural science,” 4th eds., McGraw-Hill, New York, 2000.
  11. [11] J. Bossy, “Atlas of Neuroanatomy and Special Sense Organs,” W. B. Saunders, 1970.
  12. [12] S. G. Waxman, “Correlative Neuroanatomy,” 22nd eds., a LANGE medical book, 1995.
  13. [13] H. Kimura, S. Akiyama, and K. Sakurama, “Realization of dynamic walking and running of the quadruped using neural oscillator,” Autonomous Robots, Vol.7, pp. 247-258, 1999.
  14. [14] S. Miyakoshi, G. Taga, Y. Kuniyoshi, and A. Nagakubo, “Three-dimensional bipedal stepping motion using neural oscillators – towards humanoid motion in the real world,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 1998, pp. 84-89.
  15. [15] A. J. Ijspeert, “A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander,” Biological Cybernetics, Vol.84, pp. 331-348, 2001.
  16. [16] K. Tsujita, K. Tsuchiya, and A. Onat, “Adaptive gait pattern control of a quadruped locomotion robot,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, pp. 2318-2325.
  17. [17] M. A. Lewis et al., “An in silico central pattern generator: silicon oscillator, coupling, entrainment, and physical computation,” Biological Cybernetics, Vol.88, pp. 137-151, 2003.
  18. [18] Y. Koike, and M. Kawato, “Estimation of arm posture in 3-D space from surface EMG signals using a neural network model,” Trans. Inst. Electron., Inform., Commun. Eng., Vol.J77-D-II, No.1, pp. 193-203, 1994.
  19. [19] O. Fukuda, T. Tsuji, M. Kaneko, and A. Otsuka, “A Human-Assisting Manipulator Teleoperated by EMG Signals and Arm Motions,” IEEE Transactions on Robotics and Automation, Vol.19, No.2, pp. 210-222, 2003.
  20. [20] G. Taga, “A model of the neuro-musculo-skeletal system for human locomotion II. Real-time adaptability under various constraints,” Biological Cybernetics, Vol.73, pp. 113-121, 1995.
  21. [21] N. Ogihara, and N. Yamazaki, “Generation of Human Bipedal Locomotion by a Bio-Mimetic Neuro-Musculo-Skeletal Model,” Biological Cybernetics, Vol.84, pp. 1-11, 2001.

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

Copyright© 2005 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. 22, 2024