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Using Markov Logic Network for On-Line Activity Recognition from Non-visual Home Automation Sensors

  • Pedro Chahuara
  • Anthony Fleury
  • François Portet
  • Michel Vacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7683)

Abstract

This paper presents the application of Markov Logic Networks(MLN) for the the recognition of Activities of Daily Living (ADL) in a smart home. We describe a procedure that uses raw data from non visual and non wearable sensors in order to create a classification model leveraging logic formal representation and probabilistic inference. SVM and Naive Bayes methods were used as baselines to compare the performance of our implementation, as they have proved to be highly efficient in classification tasks. The evaluation was carried out on a real smart home where 21 participants performed ADLs. Results show not only the appreciable capacities of MLN as a classifier, but also its potential to be easily integrable into a formal knowledge representation framework.

Keywords

Activity Recognition Markov Logic Network Support Vector Machine Smart Home Ambient Assisted Living 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Chahuara
    • 1
  • Anthony Fleury
    • 2
    • 3
  • François Portet
    • 1
  • Michel Vacher
    • 1
  1. 1.Laboratoire d’Informatique de GrenobleGrenoble 1/Grenoble INP/CNRS UMR 5217GrenobleFrance
  2. 2.Univ. Lille Nord de FranceLilleFrance
  3. 3.EMDouai, IADouai CedexFrance

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