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Related Experiment Video

Updated: Jun 13, 2026

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A Video-Based Measurement Framework for Chewing-Event Detection Using 3D Facial Landmark Dynamics and sEMG-Based

Nicola Giulietti1, Carlotta Massotti2, Hermes Giberti1

  • 1Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Via Adolfo Ferrata 5, 27100 Pavia, Italy.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a non-contact, video-based system for accurately measuring chewing events during natural eating. The framework uses facial landmark dynamics and recurrent neural networks, achieving high accuracy and real-time performance for feeding behavior monitoring.

Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Accurate measurement of chewing events is crucial for monitoring feeding behavior and masticatory function.
  • Existing methods often require contact sensors, wearables, or manual annotation, limiting unobtrusive monitoring.
  • There is a need for non-contact, automated methods for chewing event detection in naturalistic settings.

Purpose of the Study:

  • To develop and validate a non-contact, video-based framework for detecting chewing events during natural eating.
  • To utilize normalized 3D facial landmark dynamics and recurrent temporal modeling for chewing event detection.
  • To assess the system's accuracy, robustness, and real-time applicability compared to existing methods.

Main Methods:

Keywords:
3D facial landmarksMonte-Carlo uncertainty evaluationchewing event detectionmasticatory activityreal-time monitoringsurface electromyographyvideo-based measurement system

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  • A novel framework employing frontal facial video analysis was developed.
  • Normalized 3D facial landmark dynamics were extracted and processed using recurrent neural networks.
  • Physiologically grounded reference labels were derived from synchronized electromyography (EMG) during real-meal sessions.

    • Main Results:

    • The video-based method achieved high accuracy on an independent test set, with a mean absolute error of 4.4 chews/session.
    • The proposed approach demonstrated significantly lower counting errors compared to a rule-based video method, especially during concurrent activities.
    • The system achieved real-time performance (mean latency < 9 ms on CPU, < 7 ms on CUDA) and was implemented as an Android application.

    Conclusions:

    • The non-contact, video-based framework offers an accurate and unobtrusive method for chewing event detection.
    • The system demonstrates superior performance over rule-based video methods, particularly in complex eating scenarios.
    • The real-time capabilities and mobile implementation support practical deployment for feeding behavior analysis.