Context Cells: Towards Lifelong Learning in Activity Recognition Systems

  • Alberto Calatroni
  • Claudia Villalonga
  • Daniel Roggen
  • Gerhard Tröster
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5741)


A robust activity and context-recognition system must be capable of operating over a long period of time, exploiting new sources of information as they become available and evolving in an autonomous manner, coping with user variability and changes in the number and type of available sensors. In particular, wearable and ambient nodes should be trained lifelong, as new context instances naturally arise, and the labeling of the instances should be carried out ideally with no user intervention. In this paper we show by means of an experiment and simulations that we can indeed achieve lifelong learning and automatic labeling by using Context Cells, an architecture capable of sensing, learning, classifying data and exchanging information.


Sensor Node Activity Recognition Lifelong Learn Ground Truth Label Context Cell 
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 2009

Authors and Affiliations

  • Alberto Calatroni
    • 1
  • Claudia Villalonga
    • 1
    • 2
  • Daniel Roggen
    • 1
  • Gerhard Tröster
    • 1
  1. 1.Wearable Computing LaboratoryETH ZürichSwitzerland
  2. 2.SAP ResearchCEC ZürichSwitzerland

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