Abstract
The task is to monitor walking patterns and give early warning of falls using foot switch and mercury trigger sensors. We describe a dynamic belief network model for fall diagnosis which, given evidence from sensor observations, outputs beliefs about the current walking status and makes predictions regarding future falls. The model represents possible sensor error and is parametrised to allow customisation to the individual being monitored.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
P. Dagum and A. Galper. Forecasting sleep apnea with dynamic network models. In Proceedings of the Ninth Conference on Uncertainty in AI, pages 64–71, 1993.
P. Dagum, A. Galper, and E. Horvitz. Dynamic network models for forecasting. In Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, pages 41–48, 1992.
James Davies. Fall diagnosis with a mobile data network. Unpublished BCSE Honours Report, Dept. of Electrical Engineering, Monash University, 1995.
Thomas Dean and Michael P. Wellman. Planning and control. Morgan Kaufman Publishers, San Mateo, Ca., 1991.
Jeff Forbes, Tim Huang, Keiji Kanazawa, and Stuart Russell. The BATMobile: Towards a bayesian automated taxi. In Proceedings of the 14th Int. Joint Conf. on Artificial Intelligence (IJCAI'95), pages 1878–1885, 1995.
U. Kjærulff. A computational scheme for reasoning in dynamic probabilistic networks. In Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, pages 121–129, 1992.
A. E. Nicholson and J. M. Brady. The data association problem when monitoring robot vehicles using dynamic belief networks. In Proc. of the 10th European Conf. on Artificial Intelligence (ECAI-92), pages 689–693, 1992.
A. E. Nicholson and J. M. Brady. Sensor validation using dynamic belief networks. In Proceedings of the 8th Conference on Uncertainty in Artificial Intelligence, pages 207–214, 1992.
A. E. Nicholson and J. M. Brady. Dynamic belief networks for discrete monitoring. IEEE Systems, Man and Cybernetics, 24(11), 1994.
A.E. Nicholson. A case study in dynamic belief networks: monitoring walking, fall prediction and detection. Technical Report Technical Report 96/251, Department of Computer Science, Monash University, 1996.
Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, Ca., 1988.
Gregory M. Provan. Tradeoffs in constructing and evaluating temporal influence diagrams. In Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, pages 40–47, 1993.
David Pynadeth and Michael P. Wellman. Accounting for context in plan recogniition, with application too traffic monitoring. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pages 472–481, 1995.
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, 1994.
Sampath Srinivas and Jack Breese. Ideal: Influence diagram evaluation and analysis in lisp. Technical Report Technical Memorandum No. 23, Rockwell International Science Center, 1989.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nicholson, A.E. (1996). Fall diagnosis using dynamic belief networks. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_18
Download citation
DOI: https://doi.org/10.1007/3-540-61532-6_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61532-3
Online ISBN: 978-3-540-68729-0
eBook Packages: Springer Book Archive