Representing and Learning Human Behavior Patterns with Contextual Variability

  • Paula Lago
  • Claudia Roncancio
  • Claudia Jiménez-Guarín
  • Cyril Labbé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10438)


For Smart Environments used for elder care, learning the inhabitant’s behavior patterns is fundamental to detect changes since these can signal health deterioration. A precise model needs to consider variations implied by the fact that human behavior has an stochastic nature and is affected by context conditions. In this paper, we model behavior patterns as usual activity start times. We introduce a Frequent Pattern Mining algorithm to estimate probable start times and their variations due to context conditions using only one single scan of the activity data stream. Experimentation using the Aruba CASAS and the ContextAct@A4H datasets and comparison with a Gaussian Mixture Model show our proposition provides adequate results for smart home environments domains with a lower computational time complexity. This allows the evaluation of behavior variations at different context dimensions and varied granularity levels for each of them.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Paula Lago
    • 1
  • Claudia Roncancio
    • 2
  • Claudia Jiménez-Guarín
    • 1
  • Cyril Labbé
    • 2
  1. 1.Universidad de Los AndesBogotáColombia
  2. 2.Univ. of Grenoble Alpes, CNRS, Grenoble INP, LIGGrenobleFrance

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