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Hand Tension as a Gesture Segmentation Cue

  • P. A. Harling
  • A. D. N. Edwards
Conference paper

Abstract

Hand gesture segmentation is a difficult problem that must be overcome if gestural interfaces are to be practical. This paper sets out a recognition-led approach that focuses on the actual recognition techniques required for gestural interaction. Within this approach, a holistic view of the gesture input data stream is taken that considers what links the low-level and high-level features of gestural communication. Using this view, a theory is proposed that a state of high hand tension can be used as a gesture segmentation cue for certain classes of gestures. A model of hand tension is developed and then applied successfully to segment two British Sign Language sentence fragments.

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References

  1. [1]
    T. Baudel and M. Beaudouin-Lafon. CHARADE: Remote control of objects using free-hand gestures. Communications of the ACM, 36 (7): 28–35, 1993.CrossRefGoogle Scholar
  2. [2]
    R. Beale and A. D. N. Edwards. Recognising postures and gestures using neural networks. In R. Beale and J. Finlay, editors, Neural Networks and Pattern Recognition in Human-Computer Interaction, pages 163–169. Ellis Horwood, New York, 1992.Google Scholar
  3. [3]
    M. Bordegoni and M. Hemmje. A dynamic gesture language and graphical feedback for interaction in a 3D user interface. EUROGRAPHICS’93, 12 (3): C1 — C11, 1993.Google Scholar
  4. [4]
    A. Braffort, C. Collet, and D. Teil. Anthropomorphic model for hand gesture interface. In CHI’94 Conference Companion,pages 259–260, Boston, MA, 1994. ACM Press, New York, NY.Google Scholar
  5. [5]
    S. S. Fels and G. E. Hinton. Glove-Talk: A neural network interface between a data-glove and a speech synthesizer. IEEE Transactions on Neural Networks, 4 (1): 2–8, 1993.CrossRefGoogle Scholar
  6. [6]
    P. A. Harling. Gesture input using neural networks. BEng project report, Department of Computer Science, University of York, Heslington, York, YO1 5DD, UK, Mar. 1993.Google Scholar
  7. [7]
    J. Kramer. The Talking Glove in action. Communications of the ACM, 32 (4): 515, 1989.Google Scholar
  8. [8]
    J. S. Lipscomb. A trainable gesture recognizer. Pattern Recognition, 24 (9): 895–907, 1991.CrossRefGoogle Scholar
  9. [9]
    K. Murakami and H. Taguchi. Gesture recognition using recurrent neural networks. In CHI ’91 Proceedings, pages 237–242, 1991.Google Scholar
  10. [10]
    D. Rubine. Specifying gestures by example. Computer Graphics, 25 (4): 329–337, 1991.CrossRefGoogle Scholar
  11. [11]
    T. Takahashi and F. Kishino. A hand gesture recognition method and its application. Systems and Computers in Japan, 23 (3): 38–48, 1992.CrossRefGoogle Scholar
  12. [12]
    C. Trevarthen. Form, significance and psychological potential of hand gestures of infants. In J.-L. Nespoulous, P. Perron, and A. R. Lecours, editors, The Biological Foundations of Gestures: Motor and Semiotic Aspects, pages 149–202. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1986.Google Scholar

Copyright information

© Springer-Verlag London 1997

Authors and Affiliations

  • P. A. Harling
    • 1
  • A. D. N. Edwards
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK

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