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Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
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Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review.

Hamid Heydarian1, Marc Adam2,3, Tracy Burrows4,5

  • 1School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW 2308, Australia. hamid.heydarian@uon.edu.au.

Nutrients
|May 30, 2019
PubMed
Summary

Wearable motion sensors can objectively assess eating behavior. Recent advancements in machine learning, particularly deep learning, show promise for accurate dietary monitoring using sensor data.

Keywords:
accelerometereating activity detectiongyroscopehand-to-mouth movementwrist-mounted motion tracking sensor

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Nutritional Science

Background:

  • Wearable motion tracking sensors are increasingly utilized for physical activity monitoring.
  • These sensors are gaining traction for objective dietary monitoring research.
  • Upper limb motion tracking offers a novel approach to assess eating behavior.

Purpose of the Study:

  • To synthesize existing research on using upper limb motion tracking sensors for objective eating behavior assessment.
  • To review the combination of motion sensors with other technologies like cameras and microphones.
  • To identify trends and future directions in sensor-based dietary monitoring.

Main Methods:

  • A systematic review of eleven electronic databases was conducted, yielding 69 relevant studies.
  • Studies published up to January 2019 were included, with a significant portion (28) published since 2017.
  • Data extraction focused on sensor types (accelerometers, gyroscopes), device types (smartwatches, standalone chipsets), and machine learning algorithms used.

Main Results:

  • Accelerometers, often worn on the wrist, were the most common sensors, frequently combined with gyroscopes.
  • Commercial smartwatches and fitness bands were prevalent, alongside professional devices and standalone chipsets.
  • Support Vector Machine (SVM), Random Forest, and Decision Tree were common machine learning algorithms, with Deep Learning emerging recently.

Conclusions:

  • Models considering sequential data, such as Hidden Markov Models (HMM) and Deep Learning, demonstrate potential for accurate eating activity detection.
  • Despite variations in datasets limiting direct model comparison, temporal context is crucial for effective detection.
  • Future research should explore emerging applications and refine sensor-based dietary assessment methodologies.