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A Modular and Distributed Bayesian Framework for Activity Recognition in Dynamic Smart Environments

  • Arun Kishore Ramakrishnan
  • Davy Preuveneers
  • Yolande Berbers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8309)

Abstract

Conditional dependencies between the human activities and different contexts (such as location and time) in which they emerge, are well known and have been utilized in the modern Ambient Intelligence (AmI) applications. But the rigid topology of the inference models in most of the existing systems adversely affects their flexibility and ability to handle inherent sensor ambiguities. Hence, we propose a framework for activity recognition suitable for a distributed and evolving smart environment. On the one hand, the framework exhibits flexibility to dynamically add and remove contexts through autonomic learning of individual contexts capitalizing the spatially distributed AmI infrastructures. On the other hand, it shows resilience to missing data by boot-strapping and fusing multiple heterogeneous context information.

Keywords

User contexts and activity recognition distributed software and frameworks Smart home/office Bayesian algorithm 

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References

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Arun Kishore Ramakrishnan
    • 1
  • Davy Preuveneers
    • 1
  • Yolande Berbers
    • 1
  1. 1.iMinds-DistriNetKU LeuvenBelgium

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