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JACIII Vol.13 No.3 pp. 222-229
doi: 10.20965/jaciii.2009.p0222
(2009)

Paper:

An Adaptive Muscular Force Generation Mechanism Based on Prior Information of Handling Object

Kosuke Sekiyama*, Masahiro Ito*, Toshio Fukuda*, Takashi Suzuki**, and Koshiro Yamashita**

*Dep. of Micro-Nano Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-city, Aichi 464-8603, Japan

** TOKAI RIKA CO., LTD., 3-260, Toyota, Oguchi-cho, Niwa-gun, Aichi 480-0195, Japan

Received:
November 25, 2008
Accepted:
February 9, 2009
Published:
May 20, 2009
Keywords:
surface EMG signals, prediction, finger force, color effect, size-weight illusion
Abstract
Evaluating the influences of human-machine interface (HMI) visual information is vital to developing the user-oriented and human-friendly equipment, robots, etc. We define HMI visual information as prior information, such as size, color, shape, etc. The relationship between prior information and the Kansei feeling is evaluated by surface electromyogram (sEMG). This study deals with object-grasping motion and measures sEMG signals during the motion. Prediction on object-grasping motion is predicted from sEMG signals and defined as Force Prediction (FP). Differences between prediction of HMI operation and actual results are assumed to influence on Kansei feeling concerning the operation. Subjects given different prior information calculate FP about plastic bottles when grasping them. Experimental results show that the FP differs even though the plastic bottles have the same weight. The influence of prior information on FP is visually plotted in a three-dimensional map which is called Size-Color-iEMG map, and its application is to HMI design.
Cite this article as:
K. Sekiyama, M. Ito, T. Fukuda, T. Suzuki, and K. Yamashita, “An Adaptive Muscular Force Generation Mechanism Based on Prior Information of Handling Object,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.3, pp. 222-229, 2009.
Data files:
References
  1. [1] R. C. Miall and D. M. Wolpert, “Forward models for physiological motor control,” Neural Networks, Vol.9, No.8, pp. 1265-1279, 1996.
  2. [2] B. M. Quaney, D. L. Rotella, C. Peterson, and K. J. Cole, ”Sensorimotor memory for fingertip forces: Evidence for a task-independent motor memory,” The Journal of Neuroscience, Vol.23, pp. 1981-1986, 2003.
  3. [3] C. Schmitz, P. Jenmalm, H. H. Ehrsson, and H. Forssberg, “Brain activity during predictable and unpredictable weight changes when lifting objects,” Journal of Neurophysiology, Vol.93, pp. 1498-1509, 2005.
  4. [4] Y. Koike, J. Kim, and S. Duk, “Role of stiffness in weight perception,” Japanese Psychological Research, Vol.3, pp. 174-187, 2006.
  5. [5] S. Kawai, F. Henigman, CL. MacKenzie, and et al, “A reexamination of the size-weight illusion induced by visual size cues,” EXPERIMENTAL BRAIN RESEARCH, Vol.179, pp. 443-456, 2007.
  6. [6] J.-P. Bresciani, K. Drewing, and M. Ernst, “Risk analysis for multidimensional illusions for multimodal systems,” Proc. of the workshop “Touch and Haptics,” Vol. Workpackage 5, pp. D5.4-1, 2003.
  7. [7] J.-P. Bresciani, K. Drewing, and M. Ernst, “Risk analysis for multidimensional illusions for multimodal systems 02,” Proc. of the workshop “Touch and Haptics,” Vol. Workpackage 5, pp. D5.4-2, 2003.
  8. [8] J. E. De Camp, “The influence of color on apparent weight: A preliminary study,” Journal of Experimental Psychology, Vol.62, pp. 347-370, 1917.
  9. [9] P. Jenmalm and R. S. Johansson, “Visual and somatosensory information about object shape control manipulative fingertip forces,” The Journal of Neuroscience, Vol.17, pp. 4486-4499, 1997.
  10. [10] P. Jenmalm, S. Dahlstedt, and R. S. Johamsson, “Visual and tactile information about object-curvature control fingertip forces and grasp kinematics in human dexterous manipulation,” The American Physiological Society, Vol.84, pp. 2984-2997, 2000.
  11. [11] M. Taira, S. Mine, A.P. Georgopoulos, A. Murata, and H. Sakata, “Parietal cortex neurons of the monkey related to the visual guidance of hand movement,” EXPERIMENTAL BRAIN RESEARCH, Vol.83, pp. 29-36, 1990.
  12. [12] K. Sekiyama, M. Ito, T. Fukuda, T. Suzuki, and K. Yamashita, “Quantitative evaluation of feeling in switch-pressing motion based on human biometric information,” Proc. of the 17th IEEE Int. Symposium on Robot and Human Interactive Communication (RO-MAN2008), pp. 459-464, 2008.
  13. [13] Eui S. Jung and J. Choe, “Human reach posture prediction based on psychophysical discomfort,” Int. Journal of Industrial Ergonomics, Vol.18, pp. 173-179, 1996.
  14. [14] X. Hu, Z. Wang, and X. Ren, “Classification of surface emg signal using relative wavelet packet energy,” Computer Methods and Programs in Biomedicine, Vol.79, pp. 189-195, 2005.
  15. [15] J. H. T. Viitasalo and P. V. Komi, “Signal characteristics of emg during fatigue,” European Journal of Applied Physiology, Vol.37, pp. 111-121, 1977.
  16. [16] T. Sadoyama and H. Miyano, “Frequency analysis of surface emg to evaluation of muscle fatigue,” European Journal of Applied Physiology, Vol.47, pp. 239-246, 1981.

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