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International Conference on User Modeling, Adaptation, and Personalization

UMAP 2011: User Modeling, Adaption and Personalization pp 219–230Cite as

A Dynamic Sliding Window Approach for Activity Recognition

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6787)

Abstract

Human activity recognition aims to infer the actions of one or more persons from a set of observations captured by sensors. Usually, this is performed by following a fixed length sliding window approach for the features extraction where two parameters have to be fixed: the size of the window and the shift. In this paper we propose a different approach using dynamic windows based on events. Our approach adjusts dynamically the window size and the shift at every step. Using our approach we have generated a model to compare both approaches. Experiments with public datasets show that our method, employing simpler models, is able to accurately recognize the activities, using fewer instances, and obtains better results than the approaches used by the datasets authors.

Keywords

  • Human Activity Recognition
  • Sliding Window
  • Sensor Networks
  • Wearable Systems
  • Ubiquitous Computing

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  • DOI: 10.1007/978-3-642-22362-4_19
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Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad Carlos III de Madrid, Avda. de la Universidad. 30, 28911, Leganés, Madrid, Spain

    Javier Ortiz Laguna, Angel García Olaya & Daniel Borrajo

Authors
  1. Javier Ortiz Laguna
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  2. Angel García Olaya
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  3. Daniel Borrajo
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, 4-192 Keller Hall, 55455, Minneapolis, MN, USA

    Joseph A. Konstan

  2. E.T.S. Ing. Informatica, Universidad de Málaga, Boulevard Lois Pasteur, 35, 29071, Malaga, Spain

    Ricardo Conejo

  3. EPS Edifici P-IV ( D.208), University of Girona, Campus de Montilivi, 17071, Girona, Spain

    José L. Marzo

  4. Telefonica Research R& D, Via Augusta, 177, 08021, Barcelona, Catalonia, Spain

    Nuria Oliver

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Ortiz Laguna, J., Olaya, A.G., Borrajo, D. (2011). A Dynamic Sliding Window Approach for Activity Recognition. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-22362-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22361-7

  • Online ISBN: 978-3-642-22362-4

  • eBook Packages: Computer ScienceComputer Science (R0)