single-rb.php

JRM Vol.18 No.5 pp. 626-633
doi: 10.20965/jrm.2006.p0626
(2006)

Paper:

Novel Human Interface for Game Control Using Voluntarily Generated Biological Signals

Keisuke Shima*, Masaru Okamoto*, Nan Bu**, and Toshio Tsuji*

*Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

**On-Site Sensing and Diagnosis Research Laboratory, National Institute of AIST, 807-1 Shuku-machi, Tosu, Saga 841-0052, Japan

Received:
March 8, 2006
Accepted:
April 21, 2006
Published:
October 20, 2006
Keywords:
human interface, video game machines, biological signals, probabilistic neural network, pattern discrimination
Abstract

We propose a human interface for video game operation using voluntarily generated biological signals as input. The users choose specific input signals and configure signal measurement based on preferences, physical condition (disabled or not), and degree of disability. Based on input signals, the intended user operations are estimated with a probabilistic neural network (PNN), and then control commands are determined. Our proposed interface enables individuals even with severe physical disabilities to maneuver video games. Experiments confirmed the feasibility of our designed interface by subjects suffering from cervical spine injury.

Cite this article as:
Keisuke Shima, Masaru Okamoto, Nan Bu, and Toshio Tsuji, “Novel Human Interface for Game Control Using Voluntarily Generated Biological Signals,” J. Robot. Mechatron., Vol.18, No.5, pp. 626-633, 2006.
Data files:
References
  1. [1] Ministry of Health, Labour and Welfare, “The fiscal year 2001 survey on actual condition of disabled child and person,” (in Japanese).
    http://www.mhlw.go.jp/houdou/2002/08/h0808-2.html
  2. [2] M. Suzuki, Y. Niida, G. Kamiyama, S. Yamashita, Y. Shiota, T. Tamagaki, and E. Ito, “The interface of a consumer game for a handicapped parson,” Transactions of the Japanese Society for Medical and Biological Engineering, Vol.42, Suppl.2, p. 140, 2004 (in Japanese).
  3. [3] J. B. Lopes, “Designing User Interfaces for Severely Handicapped Persons,” Workshop on Universal Accessibility of Ubiquitous Computing: Providing for the Elderly, pp. 100-106, 2001.
  4. [4] E. Ito, “Multi-modal Interface with Voice and Head Tracking for Multiple Home Appliances,” Proceedings of INTERACT2001 8th IFIP TC. 13 Conference on Human-Computer Interaction, pp. 727-728, 2001.
  5. [5] O. Fukuda, S. Fujita, and T. Tsuji, “A Substitute Vocalization System Based on EMG Signals,” The IEICE Transactions on Information and Systems, D-II, Vol.J88, No.1, pp. 105-112, 2005 (in Japanese).
  6. [6] S. Iga and F. Higuchi, “Kirifuki: Inhaling and Exhaling Interaction for Entertainment Systems,” Transaction of the Virtual Reality Society of Japan, Vol.7, No.4, pp. 445-452, 2002 (in Japanese).
  7. [7] M. Betke, J. Gips, and P. Fleming, “The Camera Mouse: Visual Tracking of Body Features to Provide Computer Access for People With Severe Disabilities,” IEEE Transactions on Neural System and Rehabilitation Engineering, Vol.10, No.1, pp. 1-10, 2002.
  8. [8] C. S. Lin, C. C. Huan, C. N. Chan, M. S. Yeh, and C. C. Chiu, “Design of a computer game using an eye-tracking device for eye’s activity rehabilitation,” Optics and Lasers in Engineering, Vol.42, No.1, pp. 91-108, 2004.
  9. [9] R. Krepki, B. Blankertz, G. Gurio, and K. R. Muller, “The Berlin Brain-Computer Interface (BBCI): towards a new communication channel for online control of multimedia applications and computer games,” 9th International Conference on Distributed Multimedia Systems (DMS’03), pp. 237-244, 2003.
  10. [10] D. Specht, “Probabilistic Neural Networks,” Neural Networks, Vol.3, No.1, pp. 109-118, 1990.
  11. [11] T. Tsuji, O. Fukuda, H. Ichinobe, and M. Kaneko, “A Log-Linearized Gaussian Mixture Network and Its Application to EEG Pattern Classification,” IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol.29, No.1, pp. 60-72, 1999.
  12. [12] T. Tsuji, H. Ichinobe, K. Ito, and M. Nagamachi, “Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy,” Transactions of the Society of Instrument and Control Engineers, Vol.29, No.10, pp. 1213-1220, 1993 (in Japanese).
  13. [13] Measurand Inc., S700/S720 Joint Angle SHAPE SENSOR INSTRUCTION MANUAL, pp. 4-6, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021