Fall diagnosis using dynamic belief networks

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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.

Original languageEnglish
Title of host publicationPRICAI 1996
Subtitle of host publicationTopics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings
PublisherSpringer
Pages206-217
Number of pages12
ISBN (Print)3540615326, 9783540615323
DOIs
Publication statusPublished - 1 Jan 1996
EventPacific Rim International Conference on Artificial Intelligence 1996 - Cairns, Australia
Duration: 26 Aug 199630 Aug 1996
Conference number: 4th
https://link.springer.com/book/10.1007/3-540-61532-6 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1114
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific Rim International Conference on Artificial Intelligence 1996
Abbreviated titlePRICAI 1996
CountryAustralia
CityCairns
Period26/08/9630/08/96
Internet address

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