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Velocity profile based recognition of dynamic gestures with discrete Hidden Markov Models

  • Frank G. Hofmann
  • Peter Heyer
  • Günter Hommel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1371)

Abstract

In this paper we present a method for the recognition of dynamic gestures with discrete Hidden Markov Models (HMMs) from a continuous stream of gesture input data. The segmentation problem is addressed by extracting two velocity profiles from the gesture data and using their extrema as segmentation cues. Gestures are captured with a TUB-SensorGlove. The paper focuses on the description of the gesture recognition method (including data preprocessing) and describes experiments for the evaluation of the performance of the recognition method. The paper combines and further develops ideas from some of our previous work.

Keywords

Hide Markov Model Recognition Rate Gesture Recognition Observation Symbol Dynamic Gesture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    M. Davis, J. Skupien (Eds.): Body Movement and Nonverbal Communication — An Annotated Bibliography, 1971–1981, Indiana University Press, Bloomington, 1982Google Scholar
  2. 2.
    R. O. Duda, P. E. Hart: Pattern classification and scene analysis. Wiley-Interscience, New York, 1973zbMATHGoogle Scholar
  3. 3.
    A. D. N. Edwards: Progress in Sign Language Recognition, this volumeGoogle Scholar
  4. 4.
    K. Fukunaga: Introduction to statistical pattern recognition. 2nd ed., Academic Press, London, 1990zbMATHGoogle Scholar
  5. 5.
    GW ’96: Progress in gestural interaction: Proceedings of Gesture Workshop ’96, Philip A. Harling and Alistair D. N. Edwards (eds.), Springer, London, 1997Google Scholar
  6. 6.
    F. G. Hofmann: Entwurf und Implementierung eines Sensorhandschuhs zur Steuerung der USC/Belgrad Hand. Studienarbeit am Institut für Technische Informatik der TU Berlin, November 1991.Google Scholar
  7. 7.
    F. G. Hofmann, G. Hommel: Analyzing Human Gestural Motions using Acceleration Sensors. In [5], pp. 39–59, 1996Google Scholar
  8. 8.
    G. Hommel, F. G. Hofmann, J. Henz: The TU Berlin High-Precision Sensor Glove. In Proceedings of the WWDU’94, Fourth International Scientific Conference, University of Milan, Milan/Italy, Vol. 2, pp. F47–F49, 1994Google Scholar
  9. 9.
    T. S. Huang, V. Pavlovic: Hand Gesture Modeling, Analysis, and Synthesis. In [11], pp. 73–79, 1995Google Scholar
  10. 10.
    ICAFGR 1996: M. E. Kavanaugh, editor, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, Killington, Vermont, October 14–16, 1996, IEEE Computer Society Press, Los Alamitos, CAGoogle Scholar
  11. 11.
    IWAFGR 1995: M. Bichsel, editor, Proceedings of the International Workshop on Automatic Face-and Gesture-Recognition, Zürich, Switzerland, June 26–28, 1995.Google Scholar
  12. 12.
    R. S. Kalawsky: The Science of Virtual Reality and Virtual Environments, Addison-Wesley, Reading, Massachusetts, 1993Google Scholar
  13. 13.
    J. S. Lipscomb: A Trainable Gesture Recognizer, Pattern Recognition, Vol. 24, No. 9, pp. 895–907, 1991CrossRefGoogle Scholar
  14. 14.
    L. R. Rabiner: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, Vol. 77, No. 2, February 1989, pp. 257–286, 1989CrossRefGoogle Scholar
  15. 15.
    O. Rothbart: Entwurf und Implementierung eines Systems zur Erkennung einfacher dynamischer Gebärden mittels ausgewählter Klassifikatoren, Diplomarbeit, Institut für Technische Informatik der TU Berlin, 1995Google Scholar
  16. 16.
    T. Starner, A. Pentland: Visual Recognition of American Sign Language Using Hidden Markov Models. In [11], pp. 189–194Google Scholar
  17. 17.
    A. D. Wilson, A. F. Bobick: Configuration States for the Representation and Recognition of Gesture. In [11], pp. 129–134Google Scholar
  18. 18.
    Zimmermann, T.G., Lanier, J., Blanchard, C., Bryson, S., Harvill, Y.: A Hand Gesture Interface Device. In Proceedings of CHI + GI ’87 Human Factors in Computing Systems, pp. 189–192, Toronto, Canada, 5–9 April. ACM press, 1987Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Frank G. Hofmann
    • 1
  • Peter Heyer
    • 1
  • Günter Hommel
    • 1
  1. 1.Department of Computer ScienceTechnical University of BerlinBerlinGermany

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