Phone based fall detection by genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{conf/mum/DauSSHH14,
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title = "Phone based fall detection by genetic programming",
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author = "Anh Hoang Dau and Flora Dilys Salim and Andy Song and
Lachlan Hedin and Margaret Hamilton",
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booktitle = "Proceedings of the 13th International Conference on
Mobile and Ubiquitous Multimedia, MUM 2014",
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publisher = "ACM",
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year = "2014",
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editor = "Arkady B. Zaslavsky and Seng W. Loke and
Lars Kulik and Evaggelia Pitoura",
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address = "Melbourne, Victoria, Australia",
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month = nov # " 25-28",
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pages = "256--257",
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keywords = "genetic algorithms, genetic programming, fall
detection, mobile sensing",
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isbn13 = "978-1-4503-3304-7",
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bibdate = "2014-11-20",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/mum/mum2014.html#DauSSHH14",
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URL = "http://dl.acm.org/citation.cfm?id=2677972",
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DOI = "doi:10.1145/2677972.2678010",
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acmid = "2678010",
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abstract = "Elderly people are prone to fall due to the high rate
of risk factors associated with ageing. Existing fall
detection systems are mostly designed for a constrained
environment, where various assumptions are applied. To
overcome these drawbacks, we opt to use mobile phones
with standard built-in sensors. Fall detection is
performed on motion data collected by sensors in the
phone alone. We use Genetic Programming (GP) to learn a
classifier directly from raw sensor data. We compare
the performance of GP with the popular approach of
using threshold-based algorithm. The result shows that
GP-evolved classifiers perform consistently well across
different fall types and overall more reliable than the
threshold-based.",
- }
Genetic Programming entries for
Anh Hoang Dau
Flora Salim
Andy Song
Lachlan Hedin
Margaret Hamilton
Citations