Transferring Knowledge of Activity Recognition across Sensor Networks

  • T. L. M. van Kasteren
  • G. Englebienne
  • B. J. A. Kröse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6030)


A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper we introduce a transfer learning method for activity recognition which allows the use of existing labelled data sets of various homes to learn the parameters of a model applied in a new home. We evaluate our method using three large real world data sets and show our approach achieves good classification performance in a home for which little or no labelled data is available.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • T. L. M. van Kasteren
    • 1
  • G. Englebienne
    • 1
  • B. J. A. Kröse
    • 1
  1. 1.Intelligent Systems Lab AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands

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