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Automatic Detection of Chewing and Swallowing.

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This study developed a novel method to automatically recognize eating behaviors like chewing and swallowing using sound analysis. This non-invasive technique offers a simpler way to monitor crucial health-related actions.

Keywords:
chewingdata augmentationeating behaviorhybrid CTC/attention modelswallowing

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Area of Science:

  • Biomedical Engineering
  • Acoustic Signal Processing
  • Health Informatics

Background:

  • Eating behaviors (chewing, swallowing) are vital for health.
  • Current data collection methods are invasive (X-rays, fiberscopes).
  • A simpler, non-invasive measurement method is needed for healthcare.

Purpose of the Study:

  • To develop an automated method for recognizing eating behaviors from sounds.
  • To enable non-invasive monitoring of chewing and swallowing patterns.

Main Methods:

  • Utilized a hybrid CTC/attention model for sound analysis.
  • Employed 2-channel microphones placed under the ear for sound recording.
  • Applied N-gram based data augmentation with weakly labeled eating sound data.

Main Results:

  • Successfully detected left chewing, right chewing, front biting, and swallowing.
  • The hybrid CTC/attention model improved detection performance by learning context.
  • Achieved similar detection accuracy for both open and closed food types.

Conclusions:

  • The developed sound-based method accurately recognizes eating behaviors non-invasively.
  • This approach provides a simpler alternative to invasive techniques for health monitoring.
  • The model's ability to learn context and handle augmented data enhances its practical application.