Abstract
We describe a general method for extending any pointer input device with an arbitrary set of commands. The proposed interface can be trained by the user to recognize certain cursor movement patterns and interpret them as special input events. Methods for extraction and recognition of such patterns are general enough to work with low-precision pointing devices, and they can be adjusted to provide computer access for people with disabilities. The core of the system is a trainable classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to complexity of the classification task. The system demonstrated good accuracy and responsiveness during extensive experiments. Some tests included a severely motion-impaired individual.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Betke, M., Gips, J., Fleming, P.: The Camera Mouse: Visual Tracking of Body Features to Provide Computer Access for People With Severe Disabilities. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10(1) (2002)
Cloud, R.L., Betke, M., Gips, J.: Experiments with a Camera-Based Human-Computer Interface System. In: Proceedings of the 7th ERCIM Workshop User Interfaces for All UI4ALL, 2002, Paris, France, October 2002, pp. 103–110 (2002)
Cun, Y.L., Bottou, L., Orr, G., Muller, K.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 5–50. Springer, Heidelberg (1998)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, New York (2001)
Fahlman, S.E., Lebiere, C.: The Cascade-Correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University, Pittsburg PA (1991)
Grauman, K., Betke, J., Lombardi, J., Gips, J., Bradski, G.: Communication via Eye Blinks and Eyebrow Raises: Video-Based Human-Computer Interfaces. Universal Access in the Information Society 2(4), 359–373 (2003)
Hecht-Nielsen, R.: Kolmogorov’s Mapping Neural Network Existence Theorem. In: Proceedings of IEEE First Annual Int. Conf. on Neural Networks, San Diego, vol. 3, pp. 11–13 (1987)
Iyer, M.S., Rhinehart, R.R.: A Method to Determine the Required Number of Neural Network Training Repetitions. In: Proceedings of IEEE Transactions on Neural Networks, vol. 10(2), pp. 427–432 (1999)
Kim, K.-N., Ramakrishna, R.S.: Vision-Based Eye-Gaze Tracking for Human Interface. In: IEEE International Conf. on Systems, Man, and Cybernetics, Tokyo, Japan (1999)
Sjogaard, S.: A Conceptual Approach to Generalization in Dynamic Neural Networks. Ph.D. Thesis, Aarhus University, Aarhus, Denmark (1991)
Widrow, B., Lehr, M.A.: 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation. Proceedings of the IEEE 78(9), 1415–1442 (1990)
Yeung, D.-Y.: Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks. In: Moody, J.E., Hanson, S.J., Lippman, R.P. (eds.) Advances in Neural Information Processing Systems, San Mateo, CA, vol. 4, pp. 1072–1079. Morgan Kaufman Publishers, San Francisco (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gusyatin, O., Urinson, M., Betke, M. (2004). A Method to Extend Functionality of Pointer Input Devices. In: Stary, C., Stephanidis, C. (eds) User-Centered Interaction Paradigms for Universal Access in the Information Society. UI4ALL 2004. Lecture Notes in Computer Science, vol 3196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30111-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-30111-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23375-6
Online ISBN: 978-3-540-30111-0
eBook Packages: Springer Book Archive