Seminar: Categorising food intake from chewing vibration

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Some of the typical food categories to be classified.


Determining what food is consumed during a meal is a central research challenge of Automated Dietary Monitoring (ADM). During chewing, food breaks down and generates unique vibration signals which can be recorded with sensor-integrated wearables. Chewing vibration data usually contain rich information highly related to the food intake, thus can be used to infer the food categories.



Build machine learning models to classify food categories with chewing vibration data.


Learning objectives

  • Apply audio signal processing techniques.
  • Apply machine learning algorithms to solve practical classification problems.


Project type Seminar
Language English
Period Winter term 2020/21
Presense time Virtual seminar, working from remote
Useful knowledge Machine learning, signal processing
Work distribution 100% programming
Med. Eng. designation Advanced Context Recognition (ACR)
StudOn link
First meeting online-introduction-vorbesprechung-of-summer-term-2020-seminars, on 4th November 2020 at 16:15
Registration Via StudOn, obligatory after introduction


Up-to-date literature recommendations are provided during the meetings.


Final presentation and final report.


Rui Zhang

  • Job title: Researcher
  • Address:
    Henkestr. 91, Geb. 7
    91052 Erlangen
  • Phone number: +49 9131 85-23604
  • Email:

Friedrich-Alexander-Universität Erlangen-Nürnberg