Going Through Directional Changes: Evolving Human Movement Classifiers Using an Event Based Encoding

Created by W.Langdon from gp-bibliography.bib Revision:1.4128

@InProceedings{Lones:2017:GECCO,
  author =       "Michael A. Lones and Jane E. Alty and 
                 Jeremy Cosgrove and Stuart Jamieson and Stephen L. Smith",
  title =        "Going Through Directional Changes: Evolving Human
                 Movement Classifiers Using an Event Based Encoding",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1365--1371",
  size =         "7 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082490",
  DOI =          "doi:10.1145/3067695.3082490",
  acmid =        "3082490",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, directional
                 changes, dyskinesia, movement analysis, parkinson's
                 disease, time series analysis",
  month =        "15-19 " # jul,
  abstract =     "Directional changes (DC) is an event based encoding
                 for time series data that has become popular in
                 financial analysis, particularly within the
                 evolutionary algorithm community. In this paper, we
                 apply DC to a medical analytics problem, using it to
                 identify and summarise the periods of opposing
                 directional trends present within a set of
                 accelerometry time series recordings. The summarised
                 time series data are then used to train classifiers
                 that can discriminate between different kinds of
                 movement. As a case study, we consider the problem of
                 discriminating the movements of Parkinson's disease
                 patients when they are experiencing a common effect of
                 medication called levodopa-induced dyskinesia. Our
                 results suggest that a DC encoding is competitive
                 against the window-based segmentation and frequency
                 domain encodings that are often used when solving this
                 kind of problem, but offers added benefits in the form
                 of faster training and increased interpretability.",
  notes =        "Also known as \cite{Lones:2017:GTD:3067695.3082490}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Michael A Lones Jane E Alty Jeremy Cosgrove D R Stuart Jamieson Stephen L Smith

Citations