Analysis of Chewing Sounds for Dietary Monitoring

  • Oliver Amft
  • Mathias Stäger
  • Paul Lukowicz
  • Gerhard Tröster
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3660)


The paper reports the results of the first stage of our work on an automatic dietary monitoring system. The work is part of a large European project on using ubiquitous systems to support healthy lifestyle and cardiovascular disease prevention. We demonstrate that sound from the user’s mouth can be used to detect that he/she is eating. The paper also shows how different kinds of food can be recognized by analyzing chewing sounds. The sounds are acquired with a microphone located inside the ear canal. This is an unobtrusive location widely accepted in other applications (hearing aids, headsets). To validate our method we present experimental results containing 3500 seconds of chewing data from four subjects on four different food types typically found in a meal. Up to 99% accuracy is achieved on eating recognition and between 80% to 100% on food type classification.


Recognition Rate Speech Signal Audio Signal Food Type Potato Chip 
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 2005

Authors and Affiliations

  • Oliver Amft
    • 1
  • Mathias Stäger
    • 1
  • Paul Lukowicz
    • 2
  • Gerhard Tröster
    • 1
  1. 1.Wearable Computing LabSwiss Federal Institute of Technology (ETH) ZürichSwitzerland
  2. 2.Institute for Computer Systems and NetworksUniversity for Health Sciences, Medical Informatics and Technology (UMIT)Hall in TirolAustria

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