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Related Experiment Video

Updated: Jun 12, 2025

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

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Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior

Simon Stankoski1, Ivana Kiprijanovska1, Martin Gjoreski2

  • 1Emteq Ltd., Brighton, United Kingdom.

JMIR Mhealth and Uhealth
|September 26, 2024
PubMed
Summary

This study developed smart glasses that noninvasively monitor eating and chewing activities. The system accurately detects chewing segments, offering a novel approach to dietary behavior analysis for obesity and chronic disease research.

Keywords:
automatic dietary monitoringchewing detectioneating behavioreating detectionsmart glasses

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Area of Science:

  • Biomedical Engineering
  • Health Technology
  • Wearable Sensors

Background:

  • Rising obesity rates highlight the need for better dietary monitoring.
  • Traditional methods suffer from participant burden and recall bias.
  • Microlevel eating activities are crucial for understanding obesity and disease risk.

Purpose of the Study:

  • To develop an accurate, noninvasive system using smart glasses for monitoring eating and chewing.
  • To differentiate chewing from other facial activities like speaking.
  • To evaluate system performance in controlled and real-life settings.

Main Methods:

  • Utilized OCO optical sensors in smart glasses to capture facial muscle activations.
  • Employed deep learning (DL) for analyzing sensor data and distinguishing chewing.
  • Integrated a hidden Markov model to address temporal dependencies in chewing events.

Main Results:

  • Sensor data showed significant differences across activities (P<.001).
  • A convolutional long short-term memory DL model achieved an F1-score of 0.91 for chewing detection.
  • Real-life testing yielded high precision (0.95) and recall (0.82) for eating segments.

Conclusions:

  • This system offers a significant advancement in noninvasive dietary monitoring.
  • It has the potential to transform dietary data collection for health interventions.
  • Enables a deeper understanding of eating habits and their health implications.