Genetic Programming Based Activity Recognition on a Smartphone Sensory Data Benchmark

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Xie:2014:CECa,
  title =        "Genetic Programming Based Activity Recognition on a
                 Smartphone Sensory Data Benchmark",
  author =       "Feng Xie and Andy Song and Vic Ciesielski",
  pages =        "2917--2924",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming,
                 Classification, clustering and data analysis,
                 Real-world applications",
  DOI =          "doi:10.1109/CEC.2014.6900635",
  abstract =     "Activity recognition from smart phone sensor inputs is
                 of great importance to enhance user experience. Our
                 study aims to investigate the applicability of Genetic
                 Programming (GP) approach on this complex real world
                 problem. Traditional methods often require substantial
                 human efforts to define good features. Moreover the
                 optimal features for one type of activity may not be
                 suitable for another. In comparison, our GP approach
                 does not require such feature extraction process,
                 hence, more suitable for complex activities where good
                 features are difficult to be pre-defined. To facilitate
                 this study we therefore propose a benchmark of activity
                 data collected from various smartphone sensors, as
                 currently there is no existing publicly available
                 database for activity recognition. In this study, a
                 GP-based approach is applied to nine types of activity
                 recognition tasks by directly taking raw data instead
                 of features. The effectiveness of this approach can be
                 seen by the promising results. In addition our
                 benchmark data provides a platform for other machine
                 learning algorithms to evaluate their performance on
                 activity recognition.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Feng Xie Andy Song Victor Ciesielski

Citations