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  Fall Diagnosis Using Dynamic Belief Networks (1996) [3 citations — 2 self]

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by A. E. Nicholson
Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence (PRICAI-96
ftp://ftp.cs.monash.edu.au/pub/annn/pricai96.ps
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Abstract:

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. 1

Citations

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2 Fall diagnosis with a mobile data network. unpublished bcse honours report, dept. of electrical engineering, monash university – Davies - 1995
1 A case study in dynamic belief networks: monitoring walking, fall prediction and detection – Nicholson - 1996