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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or grinding food.

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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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Tooth brushing pattern classification using three-axis accelerometer and magnetic sensor for smart toothbrush.

Kang-Hwi Lee1, Jeong-Whan Lee, Kyeong-Seop Kim

  • 1Biomedical Engineering Department, Konkuk University, Chungju, Korea. kku99lkh@kku.ac.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary

This study introduces an intelligent toothbrush algorithm that accurately detects incorrect brushing patterns. The system uses motion sensors to monitor toothbrushing technique, aiding dental professionals in patient education.

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Area of Science:

  • Biomedical Engineering
  • Sensor Technology
  • Oral Hygiene

Background:

  • Intelligent toothbrushes capable of monitoring brushing motion and orientation were previously conceptualized.
  • Accurate tracking of toothbrush position and orientation within the mouth is essential for effective monitoring.

Purpose of the Study:

  • To develop and assess a tooth brushing pattern classification algorithm.
  • To enable intelligent monitoring of toothbrushing technique for improved oral hygiene instruction.

Main Methods:

  • Utilized a three-axis accelerometer and a three-axis magnetic sensor to capture toothbrush movement.
  • Applied a tilt-compensated azimuth (heading) calculation algorithm for determining inclination and orientation.
  • Evaluated the algorithm's feasibility with 8 distinct brushing patterns performed by 6 healthy subjects.

Main Results:

  • The developed algorithm demonstrated a high detection ratio of 98% for various brushing patterns.
  • Inappropriate tooth brushing patterns were identified through specific, detectable motion signatures.
  • The system successfully traced the position and orientation of the toothbrush within the oral cavity.

Conclusions:

  • The proposed intelligent toothbrush monitoring system is feasible and highly accurate.
  • This technology can significantly aid dental care professionals in educating patients on proper brushing techniques.
  • The system offers a novel approach to enhancing patient instruction in oral hygiene and brushing style.