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Soft Computing for the Analysis of People Movement Classification

  • Javier Sedano
  • Silvia González
  • Bruno Baruque
  • Álvaro Herrero
  • Emilio Corchado
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

This article presents a study of the best data acquisition conditions regarding movements of extremities in people. By using an accelerometer, there exist different ways of collecting and storing the data captured while people moving. To know which one of these options is the best one, in terms of classification, an empirical study is presented in this paper. As a soft computing technique for validation, Self-Organizing maps have been chosen due to their visualization capability. Empirical verification and comparison of the proposed classification methods are performed in a real domain, where three similar movements in the real-life are analyzed.

Keywords

Activity Recognition Soft Computing Dynamic Time Warp Artificial Intelligence Technique Well Matching Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Javier Sedano
    • 1
  • Silvia González
    • 1
  • Bruno Baruque
    • 2
  • Álvaro Herrero
    • 2
  • Emilio Corchado
    • 3
  1. 1.Instituto Tecnológico de Castilla y LeónBurgosSpain
  2. 2.Civil Engineering DepartmentUniversity of BurgosBurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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