Phone based fall detection by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/mum/DauSSHH14,

• title = "Phone based fall detection by genetic programming",
• author = "Anh Hoang Dau and Flora Dilys Salim and Andy Song and Lachlan Hedin and Margaret Hamilton",
• booktitle = "Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014",
• publisher = "ACM",
• year = "2014",
• editor = "Arkady B. Zaslavsky and Seng W. Loke and Lars Kulik and Evaggelia Pitoura",
• address = "Melbourne, Victoria, Australia",
• month = nov # " 25-28",
• pages = "256--257",
• keywords = "genetic algorithms, genetic programming, fall detection, mobile sensing",
• isbn13 = "978-1-4503-3304-7",
• bibdate = "2014-11-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/mum/mum2014.html#DauSSHH14",
• URL = "http://dl.acm.org/citation.cfm?id=2677972",
• DOI = "doi:10.1145/2677972.2678010",
• acmid = "2678010",
• 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