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JACIII Vol.15 No.8 pp. 972-979
doi: 10.20965/jaciii.2011.p0972
(2011)

Paper:

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Learning Strategy in Time-to-Contact Estimation of Falling Objects

Hiroyuki Kambara*1, Keiichi Ohishi*2, and Yasuharu Koike*1,*3,*4

*1Precision and Intelligence Lab., Tokyo Institute of Technology, R2-15, 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan

*2Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Japan

*3Solutions Research Lab., Tokyo Institute of Technology, Japan

*4CREST, JST, Japan

Received:
February 21, 2011
Accepted:
May 15, 2011
Published:
October 20, 2011
Creative Commons License
Keywords:
visuomotor learning, ball-catching movement, time-to-contact estimation
Abstract
The ability to estimate the time that remains before contact (Time-To-Contact or TTC) of a falling object is critical in daily life. In this paper, we investigated how the Central Nervous System (CNS) becomes able to estimate the TTC of a ball falling at various accelerations. According to experiments on the human ability to catch a ball falling at various accelerations, we assumed that the CNS can hold multiple TTC estimators each of which is trained for a different acceleration, and one of them is adopted for TTC estimation in a ball-catching trial. Here we made a hypothesis about how each TTC estimator is trained when there is an estimation error. (1) If the estimation error is small, the TTC estimator adopted in the trial is recalibrated. (2) If the estimation error is large, a new TTC estimator is created. To test this hypothesis, we conducted two types of ball-catching experiments in a virtual environment where the acceleration of a virtual ball is changed gradually or suddenly in each experiment. The difference in catching performances in the two experiments supported our hypothesis.
Cite this article as:
H. Kambara, K. Ohishi, and Y. Koike, “Learning Strategy in Time-to-Contact Estimation of Falling Objects,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.8, pp. 972-979, 2011.
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References
  1. [1] P.Werkhoven, H. P. Snippe, and A. Toet, “Visual processing of optic acceleration,” Vision Research, Vol.32, Issue 12, pp. 2313-2329, 1992.
  2. [2] F. Lacquaniti, M. Carrozzo, and N. Borghese, “The role of vision in tuning anticipatory motor responses of the limbs,” Multisensory Control of Movement, A. Berthoz et al. (Ed.), Oxford University Press, pp. 379-393, 1993.
  3. [3] M. Zago and F. Lacquaniti, “Visual perception and interception of falling objects: a review of evidence for an internal model of gravity,” J. of Neural Engineering, Vol.2, No.3, pp. S198-S208, 2005.
  4. [4] J. McIntyre, M. Zago, and F. Lacquaniti, “Does the brain model Newton’s laws?,” Nature Neuroscience, Vol.4, No.7, pp. 693-694, 2001.
  5. [5] M. Zago, G. Bosco, V. Maffei, M. Iosa, Y. P. Ivanenko, and F. Lacquaniti, “Internal models of target motion: expected dynamics overrides measured kinematics in timing manual interceptions,” J. of Neurophysiology, Vol.91, No.4, pp. 1620-1634, 2004.
  6. [6] P. Senot, M. Zago, F. Lacquaniti, and J. McIntyre, “Anticipating the effects of gravity when intercepting moving objects: differentiating up and down based on nonvisual cues,” J. of Neurophysiology, Vol.94, No.6, pp. 4471-4480, 2005.
  7. [7] S. Hong, J. Kim, M. Sato, and Y. Koike, “A research of human’s time-to-contact prediction model for ball catching task,” IEICE, J88-D-II, pp. 1246-1256, 2005. (in Japanese)
  8. [8] T. Kawase, H. Kambara, and Y. Koike, “A power assist device based on joint equilibrium point estimation from electromyography,” Proc. of 4th IMEKO TC 18 Symp. : Measurement, analysis and modeling of human functions, pp. 58-63, 2010.
  9. [9] 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.
  10. [10] J. F. Kaiser, “Some useful properties of Teager’s energy operators,” Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 149-152, 1993.
  11. [11] X. Li, P. Zhou, and A. S. Aruin, “Teager-Kaiser Energy Operation of surface EMG improves muscle activity onset detection,” Annals of Biomedical Engineering, Vol.35, No.9, pp. 1532-1538, 2007.
  12. [12] S. Solnik, P. Rider, K. Steinweg, P. DeVita, and T. Hortobágyi, “Teager-Kaiser energy operator signal conditioning improves EMG onset detection,” European J. of Applied Physiology, Vol.110, No.3, pp. 489-498, 2010.
  13. [13] F. A. Kagerer, J. L. Contreras-Vidal, and G. E. Stelmach, “Adaptation to gradual as compared with sudden visuo-motor distortions,” Experimental Brain Research, Vol.115, No.3, pp. 557-561, 1997.
  14. [14] Y. Sakaguchi, Y. Akashi, and M. Takano, “Visuo-motor adaptation to stepwise and gradual changes in the environment: relationship between consciousness and adaptation,” J. of Robotics and Mechatronics, Vol.13, No.6, pp. 601-613, 2001.
  15. [15] C. Michel, L. Pisella, C. Prablanc, G. Rode, and Y. Rossetti, “Enhancing visuomotor adaptation by reducing error signals: singlestep (aware) versus multiple-step (unaware) exposure to wedge prisms,” J. of Cognitive Neuroscience, Vol.19, Issue 2, pp. 341-350, 2007.
  16. [16] N. Saijo and H. Gomi, “Multiple motor learning strategies in visuomotor rotation,” PLoS One, 5-2, e9399, 2010.

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