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)


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.


Smart home Uncertainty Ontology Machine learning 



This work has been partially supported by the project 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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