A Dynamic Time Warping Approach to Real-Time Activity Recognition for Food Preparation

  • Cuong Pham
  • Thomas Plötz
  • Patrick Olivier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6439)


We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.


Recognition System Augmented Reality Activity Recognition Food Preparation Dynamic Time Warping 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cuong Pham
    • 1
  • Thomas Plötz
    • 1
  • Patrick Olivier
    • 1
  1. 1.Culture Lab, School of Computing ScienceNewcastle UniversityUnited Kingdom

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