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A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems

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Part of the Advances in Soft Computing book series (AINSC,volume 72)

Abstract

Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and continuous healthcare systems with reduced costs. A primary challenge for such assisted living systems is the automated recognition of everyday activities carried out by humans in their own home. In this work, we investigate the use of Markov Logic Networks as a framework for activity recognition within intelligent home-like environments equippedwith pervasive light-weight sensor technologies. In particular, we explore the ability of MLNs to capture temporal relations and background knowledge for improving the recognition performance.

Keywords

  • Hide Markov Model
  • Recognition Accuracy
  • Activity Recognition
  • Hard Constraint
  • Temporal Context

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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  • DOI: 10.1007/978-3-642-13268-1_34
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Helaoui, R., Niepert, M., Stuckenschmidt, H. (2010). A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems. In: Augusto, J.C., Corchado, J.M., Novais, P., Analide, C. (eds) Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010). Advances in Soft Computing, vol 72. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13268-1_34

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  • DOI: https://doi.org/10.1007/978-3-642-13268-1_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13267-4

  • Online ISBN: 978-3-642-13268-1

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