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JACIII Vol.16 No.1 pp. 108-116
doi: 10.20965/jaciii.2012.p0108
(2012)

Paper:

Subliminal Calibration for Machine Operation

Hiroshi Igarashi

Department of Electrical and Electronic Engineering, Tokyo Denki University, 2-2 Kanda-Nishiki-cho, Chiyoda-ku, Tokyo 101-8457, Japan

Received:
May 23, 2011
Accepted:
August 19, 2011
Published:
January 20, 2012
Keywords:
human-machine systems, human model, just noticeable difference, subliminal
Abstract

This paper proposes a skill assist technique without having the operator to be aware of it. Heretofore, many operation assists in a human-machine system has added artificial force in human operation input such as reactive force from obstacles. Such an approach is suitable in a particular task as simulated by the designer, because it can improve safety and efficiency, but is simultaneously hindering human learning ability. The proposed method will correct the machine dynamics of the operation subject subliminally, meaning that the operator will not be aware that it is being altered. Henceforth, it will be possible to enhance operability, without having to prevent the human learning ability. As a result of a verification experiment on 20 test subjects, it has been clarified that it is possible to enhance the operation performance without the operators knowing of the assist.

Cite this article as:
Hiroshi Igarashi, “Subliminal Calibration for Machine Operation,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.1, pp. 108-116, 2012.
Data files:
References
  1. [1] S. Yamada and T. Yamaguchi, “Training AIBO like a Dog,” Proc. of the 13th Int. Workshop on Robot and Human Interactive Communication, pp. 431-436, 2004.
  2. [2] H. Gomi and M. Kawato, “Computational Model for Adaptive Motor Control in the Cerebellum,” OYOBUTURI, Vol.61, No.10, pp. 1035-1038, 1992.
  3. [3] Y. Koike and M. Kawato, “Human Interface Using Surface EMG Signals,” IEICE Trans. on Information and Systems, Vol.J79-A, No.2, pp. 363-370, 1996.
  4. [4] K. Furuta, “Control of Pendulum: From Super Mechano-System to Human Adaptive Mechatronics,” Proc. of the 42nd IEEE Conf. on Decision and Control, Plenary Talk, pp. 1498-1507, 2003.
  5. [5] S. Suzuki, K. Kurihara, K. Furuta, and F. Harashima, “Assistance Control on a Haptic System for Human Adaptive Mechatronics,” J. of Advanced Robotics, Vol.20, No.3, pp. 323-348, 2006.
  6. [6] M. Suwa, “Embodied Aspect of Emergence of Symbols,” J. of the Society of Instrument and Control Engineers, Vol.48, No.1, 2009.
  7. [7] K. Furukawa, “The Role of Abduction in Skill Science,” Japan Society for Software Science and Technology, Vol.25, No.3, 2008.
  8. [8] B. R. Brewer, M. Fagan, R. L. Klatzky, and Y. Matsuoka, “Perceptual Limits for a Robotic Rehabilitation Environment Using Visual Feedback Distortion,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol.13, No.1, 2005.
  9. [9] R. Teghtsoonian, “On the Exponents in Sevens’s Law and the Constant in Ehrmanś Law,” Psychological Review, Vol.78, pp. 71-80, 1971.
  10. [10] H. Igarashi, A. Takeya, F. Harashima, and M. Kakikura, “Human Adaptive Assist Planning for Teleoperation,” Proc. of The 32nd Annual Conf. of the IEEE Industrial Electronics Society, pp. 4522-4527, 2006.
  11. [11] J. Albus, “A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC),” J. of Dynamic Systems, Measurement, and Control, Vol.97, pp. 220-227, 1975.
  12. [12] D.-H. Kim, J.-W. Oh, and I.-W. Lee, “Cerebellar Model Articulation Controller (CMAC) for Suppression of Structural Vibration,” J. of Computing in Civil Engineering, Vol.16, Issue 4, pp. 291-298, 2002.
  13. [13] H. Igarashi, “Subliminal Calibration for Machine Operation,” Proc. of The 14th Robotics Simposia, pp. 126-131, 2009.

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