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AGACY Monitoring: A Hybrid Model for Activity Recognition and Uncertainty Handling

  • Hela SfarEmail author
  • Amel Bouzeghoub
  • Nathan Ramoly
  • Jérôme Boudy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)

Abstract

Acquiring an ongoing human activity from raw sensor data is a challenging problem in pervasive systems. Earlier, research in this field has mainly adopted data-driven or knowledge based techniques for the activity recognition, however these techniques suffer from a number of drawbacks. Therefore, recent works have proposed a combination of these techniques. Nevertheless, they still do not handle sensor data uncertainty. In this paper, we propose a new hybrid model called AGACY Monitoring to cope with the uncertain nature of the sensor data. Moreover, we present a new algorithm to infer the activity instances by exploiting the obtained uncertainty values. The experimental evaluation of AGACY Monitoring with a large real-world dataset has proved the viability and efficiency of our solution.

Keywords

Smart home Uncertainty Ontology Machine learning 

Notes

Acknowledgements

This work has been partially supported by the project COCAPS (https://agora.bourges.univ-orleans.fr/COCAPS/) funded by Single Interministrial Fund N20 (FUI N20).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hela Sfar
    • 1
    Email author
  • Amel Bouzeghoub
    • 1
  • Nathan Ramoly
    • 1
  • Jérôme Boudy
    • 1
  1. 1.CNRS Paris SaclayTelecom SudParis, SAMOVARÉvryFrance

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