Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 21, 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

11.3K

Evaluation of the Chewing Pattern through an Electromyographic Device.

Alessia Riente1,2, Alessio Abeltino1,2, Cassandra Serantoni1,2

  • 1Metabolic Intelligence Lab, Department of Neuroscience, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Rome, Italy.

Biosensors
|July 28, 2023
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Early Sepsis Diagnosis as a Global Imperative: The Role of Raman Spectroscopy.

Journal of clinical medicine·2026
Same author

Graphene-Based 3D Scaffolds in Bone Regeneration: Emerging Opportunities for MRONJ Treatment.

Pharmaceutics·2026
Same author

Label-Free Imaging of Red Blood Cells in Gestational Diabetes: Autofluorescence Captures Oxidative Stress Beyond Membrane Fluidity.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada·2026
Same author

<i>FMR1</i> RNA interaction with DNMT1 blocks DNA methylation at the <i>FMR1 locus</i>.

NAR molecular medicine·2026
Same author

3D-Printed Multifunctional Multicompartment Polymer-Based Capsules for Tunable and Spatially Controlled Drug Release.

Journal of functional biomaterials·2025
Same author

Artificial Intelligence for Liquid Biopsy: FTIR Spectroscopy and Autoencoder-Based Detection of Cancer Biomarkers in Extracellular Vesicles.

Cells·2025

This study introduces a novel non-invasive device for analyzing chewing habits, revealing non-smokers exhibit more efficient chewing patterns with higher work values. This technology aids in understanding personalized chewing profiles and modifying unhealthy habits.

Area of Science:

  • Biomedical Engineering
  • Human Physiology
  • Nutritional Science

Background:

  • Chewing is crucial for metabolism and digestion, but existing measurement methods lack specificity.
  • Electromyography (EMG) offers accuracy but typically requires skin-attached sensors and focuses on basic chewing detection.
  • Current devices do not provide personalized chewing habit insights.

Purpose of the Study:

  • To present a novel, non-invasive device for evaluating personalized chewing styles.
  • To investigate the effects of smoking on various chewing pattern features.
  • To offer insights into chewing efficiency and habit modification.

Main Methods:

  • Development of a non-invasive device measuring chewing time, cycle time, work rate, number of chews, and work.
Keywords:
EMG devicechewing featureschewing profilemasticationsmokingstatistical analysis

More Related Videos

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.6K
An Implantable System For Chronic In Vivo Electromyography
09:52

An Implantable System For Chronic In Vivo Electromyography

Published on: April 21, 2020

10.7K

Related Experiment Videos

Last Updated: Jul 21, 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

11.3K
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.6K
An Implantable System For Chronic In Vivo Electromyography
09:52

An Implantable System For Chronic In Vivo Electromyography

Published on: April 21, 2020

10.7K
  • Case study comparing chewing patterns of smokers and non-smokers.
  • Statistical analysis of collected chewing data.

    • Main Results:

    • Non-smokers demonstrated significantly more chews and higher work values compared to smokers.
    • The study identified differences in chewing efficiency beyond just speed.
    • The device successfully captured personalized chewing metrics.

    Conclusions:

    • The developed device provides a comprehensive analysis of individual chewing patterns.
    • Smoking is associated with altered chewing efficiency, affecting multiple metrics.
    • This technology has potential for promoting healthier chewing habits through personalized feedback.