Phone based fall detection by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  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,
  URL =          "",
  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

Genetic Programming entries for Anh Hoang Dau Flora Salim Andy Song Lachlan Hedin Margaret Hamilton