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Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System.

Caroline Vieira Lucena1, Marcelo Lacerda1, Rafael Caldas1

  • 1Polytechnic School of PernambucoUniversity of PernambucoRecife-Pernambuco50100-010Brazil.

IEEE Journal of Translational Engineering in Health and Medicine
|April 14, 2018
PubMed
Summary

This study introduces a non-invasive method using inertial sensors to accurately measure jaw movements for diagnosing temporomandibular joint and orofacial disorders. Unsupervised clustering of mastication patterns shows potential for objective functional assessment.

Keywords:
Jaw movementsadaptive algorithmsartificial intelligenceinertial measurement unitmastication

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Area of Science:

  • Biomedical Engineering
  • Biomechanics
  • Medical Diagnostics

Background:

  • Musculoskeletal disorders of the temporomandibular joint (TMJ) are linked to orofacial disorders.
  • Accurate jaw movement analysis is crucial for diagnosing orofacial conditions.
  • Current methods for tracking jaw movements and analyzing mastication are subjective and rely heavily on clinician experience.

Purpose of the Study:

  • To propose an accurate, non-invasive, and low-cost method for measuring jaw movements using commercial inertial sensors.
  • To evaluate the performance of unsupervised machine learning algorithms for analyzing mastication patterns.
  • To reduce subjectivity in the assessment of masticatory function.

Main Methods:

  • Utilized a commercial low-cost inertial sensor (MPU6050) for non-invasive jaw movement tracking.
  • Compared sensor-derived jaw movement features with clinical analysis, performing statistical significance tests.
  • Applied unsupervised learning techniques, specifically Kohonen's Self-Organizing Maps and K-Means Clustering, to analyze mastication patterns.

Main Results:

  • The proposed inertial sensor method demonstrated no statistically significant difference compared to traditional clinical analysis for jaw movement features.
  • Unsupervised clustering algorithms (Self-Organizing Maps and K-Means) effectively processed jaw movement data.
  • Both clustering techniques showed encouraging results in differentiating mastication patterns between healthy subjects and simulated patients with facial trauma.

Conclusions:

  • The developed inertial sensor-based method offers an accurate and objective approach to measure jaw movements.
  • Unsupervised learning methods show significant potential for a comprehensive assessment of masticatory function.
  • The real-time application of this method can provide valuable dynamic information for healthcare professionals in diagnosing and managing orofacial disorders.