Abstract
A mathematical model of the human hand is presented. It models both the physical form and kinematic constraints of the fingers. An algorithm that takes as input six 3-dimensional coordinates, corresponding to the wrist and fingertip positions, and produces a geometric model of the hand is discussed. This algorithm uses the previously mentioned mathematical model. Results from an implementation of this algorithm are presented and discussed. The results show that fairly accurate geometric models of various hand-shapes can be produced very quickly using this algorithm. Critical analysis of the model and algorithm is undertaken and improvements suggested.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
C. Charayaphan and A. Marble. Image processing system for interpreting motion in American Sign Language. Journal of Biomedical Engineering, 14: 419–425, Sept. 1992.
B. Dorner and E. Hagen. Towards an American Sign Language interface. Artificial Intelligence Review, 8: 235–253, 1994.
S. S. Fels. Building adaptive interfaces with neural networks: The Glove-Talk pilot study. Technical Report CRG-TR-90–1, University of Toronto, Toronto, Canada, 1990.
C. Huang, C. Lien, and P. Lai. Chinese Sign Language interpretation through motion and shape analysis. In ICIP ‘82 Proceedings, pages 576–580, 1992.
M. Lacy. Artificial laboratories. AI Magazine, 1989.
J. Lee and T. Kunii. Model-based analysis of hand posture. IEEE Computer Graphics and Applications, pages 77–86, 1995.
E. Ohira, H. Sagawa, T. Sakiyama, and M. Ohki. A segmentation method for sign language recognition. IEICE Transactions on Informations and Systems, E78-D(1): 49–57, 1995.
J. Rehg and T. Kanade. Digiteyes: Vision-based hand tracking for human-computer interaction. In Proceedings of the IEEE workshop on Motion of Nonrigid Bodies, pages 16–21, 1994.
T. Takahashi and F. Kishino. Hand gesture coding based on experiments using a hand gesture interface device. SIGCHI Bulletin, 23: 67–74, Apr. 1991.
P. Vamplew and A. Adams. The SLARTI system: Applying artificial neural networks to sign language recognition. In Proceedings of the 7th Artificial Neural Network conf on technology for persons with disabilities, pages 575–579, 1992.
E. Wilson and G. Anspach. Neural networks for sign language translation. In Proceedings of the SPIE — The International Society for Optical Engineering, volume 1965, pages 589–599, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag London
About this paper
Cite this paper
Millar, R.J., Crawford, G.F. (1997). A Mathematical Model for Hand-Shape Analysis. In: Harling, P.A., Edwards, A.D.N. (eds) Progress in Gestural Interaction. Springer, London. https://doi.org/10.1007/978-1-4471-0943-3_22
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0943-3_22
Publisher Name: Springer, London
Print ISBN: 978-3-540-76094-8
Online ISBN: 978-1-4471-0943-3
eBook Packages: Springer Book Archive