Human Action Recognition from Multi-Sensor Stream Data by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Feng Xie and Andy Song and Vic Ciesielski",
  title =        "Human Action Recognition from Multi-Sensor Stream Data
                 by Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "418--427",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_42",
  size =         "10 pages",
  abstract =     "This paper presents an approach to recognition of
                 human actions such as sitting, standing, walking or
                 running by analysing the data produced by the sensors
                 of a smart phone. The data comes as streams of parallel
                 time series from 21 sensors. We have used genetic
                 programming to evolve detectors for a number of actions
                 and compared the detection accuracy of the evolved
                 detectors with detectors built from the classical
                 machine learning methods including Decision Trees,
                 Naive Bayes, Nearest Neighbour and Support Vector
                 Machines. The evolved detectors were considerably more
                 accurate. We conclude that the proposed GP method can
                 capture complex interaction of variables in parallel
                 time series without using predefined features.",
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Feng Xie Andy Song Victor Ciesielski