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Toward Recognition of Short and Non-repetitive Activities from Wearable Sensors

  • Andreas Zinnen
  • Kristof van Laerhoven
  • Bernt Schiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4794)

Abstract

Activity recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware computing. Most approaches for activity recognition focus on repetitive or long time patterns within the data. There is however high interest in recognizing very short activities as well, such as pushing and pulling an oil stick or opening an oil container as sub-tasks of checking the oil level in a car. This paper presents a method for the latter type of activity recognition using start and end postures (short fixed positions of the wrist) in order to identify segments of interest in a continuous data stream. Experiments show high discriminative power for using postures to recognize short activities in continuous recordings. Additionally, classifications using postures and HMMs for recognition are combined.

Keywords

activity recognition postures HMMs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Andreas Zinnen
    • 1
  • Kristof van Laerhoven
    • 1
  • Bernt Schiele
    • 1
  1. 1.Computer Science Department, TU Darmstadt, Hochschulstr. 10, 64289 DarmstadtGermany

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