On Using Temporal Features to Create More Accurate Human-Activity Classifiers

  • Juan Ye
  • Adrian K. Clear
  • Lorcan Coyle
  • Simon Dobson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6206)


Through advances in sensing technology, a huge amount of data is available to context-aware applications. A major challenge is extracting features of this data that correlate to high-level human activities. Time, while being semantically rich and an essentially free source of information, has not received sufficient attention for this task. In this paper, we examine the potential for taking temporal features—inherent in human activities—into account when classifying them. Preliminary experiments using the PlaceLab dataset show that absolute time and temporal relationships between activities can improve the accuracy of activity classifiers.


Temporal Feature Sensor Data Activity Recognition Absolute Time Inference Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan Ye
    • 1
  • Adrian K. Clear
    • 1
  • Lorcan Coyle
    • 2
  • Simon Dobson
    • 1
    • 2
  1. 1.CLARITY: Centre for Sensor Web TechnologiesUniversity College DublinIreland
  2. 2.Lero, The Irish Software Engineering Research CentreUniversity of LimerickIreland

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