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A novel approach for food intake detection using electroglottography.

Muhammad Farooq1, Juan M Fontana, Edward Sazonov

  • 1Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA.

Physiological Measurement
|March 28, 2014
PubMed
Summary

This study introduces an electroglottograph (EGG) device for objective food intake monitoring, replacing subjective self-reporting. The EGG method achieved 90.1% accuracy in detecting food consumption, outperforming acoustic methods.

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Area of Science:

  • Biomedical Engineering
  • Nutritional Science
  • Signal Processing

Background:

  • Current dietary monitoring methods rely on self-reporting, which is subjective and prone to inaccuracies.
  • There is a need for objective and automated methods to accurately track food intake.

Purpose of the Study:

  • To present a novel approach using an electroglottograph (EGG) device for objective food intake detection.
  • To compare the efficacy of the EGG-based method against a traditional acoustic-based method.

Main Methods:

  • Thirty subjects consumed self-selected meals during a four-visit experiment.
  • Electroglottograph (EGG) and throat microphone signals were captured during swallowing.
  • Wavelet features were extracted from 30-second signal epochs.
  • Subject-independent artificial neural network classifiers were trained to identify food intake periods.

Main Results:

  • The EGG-based method achieved an average per-epoch classification accuracy of 90.1%.
  • The acoustic-based method achieved an average per-epoch classification accuracy of 83.1%.
  • Leave-one-out cross-validation demonstrated high performance for the EGG method.

Conclusions:

  • The electroglottograph (EGG) device shows significant feasibility for objective and automatic food intake detection.
  • The EGG-based method offers superior accuracy compared to acoustic monitoring for dietary intake assessment.