Exploring the Application of Hybrid Evolutionary Computation Techniques to Physical Activity Recognition

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  author =       "Alejandro Baldominos and Carmen {del Barrio} and 
                 Yago Saez",
  title =        "Exploring the Application of Hybrid Evolutionary
                 Computation Techniques to Physical Activity
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1377--1384",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931732",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "his paper focuses on the problem of physical activity
                 recognition, i.e., the development of a system which is
                 able to learn patterns from data in order to be able to
                 detect which physical activity (e.g. running, walking,
                 ascending stairs, etc.) a certain user is

                 While this field is broadly explored in the literature,
                 there are few works that face the problem with
                 evolutionary computation techniques. In this case, we
                 propose a hybrid system which combines particle swarm
                 optimization for clustering features and genetic
                 programming combined with evolutionary strategies for
                 evolving a population of classifiers, shaped in the
                 form of decision trees. This system would run the
                 segmentation, feature extraction and classification
                 stages of the activity recognition chain.

                 For this paper, we have used the PAMAP2 dataset with a
                 basic preprocessing. This dataset is publicly available
                 at UCI ML repository. Then, we have evaluated the
                 proposed system using three different modes: a
                 user-independent, a user-specific and a combined one.
                 The results in terms of classification accuracy were
                 poor for the first and the last mode, but it performed
                 significantly well for the user-specific case. This
                 paper aims to describe work in progress, to share early
                 results an discuss them. There are many things that
                 could be improved in this proposed system, but overall
                 results were interesting especially because no manual
                 data transformation took place.",
  notes =        "Distributed at GECCO-2016.",

Genetic Programming entries for Alejandro Baldominos Gomez Carmen del Barrio Yago Saez