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Tooth Anatomy

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...

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Machine Learning-Based Toothbrushing Region Recognition Using Smart Toothbrush Holder and Wearable Sensors.

Hsuan-Chih Wang¹, Ju-Hsuan Li¹, Yen-Chen Lin¹

¹Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei City 112, Taiwan.

|December 24, 2025

Summary

This summary is machine-generated.

This study introduces a new method using machine learning and sensors to accurately identify toothbrushing areas. This technology can help monitor and improve oral hygiene practices for better overall health.

Keywords:

machine learning oral hygiene toothbrushing monitoring toothbrushing region recognition wearable sensor

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Area of Science:

Biomedical Engineering
Dental Public Health
Machine Learning Applications

Background:

Oral health is integral to systemic health, linked to conditions like cardiovascular disease and diabetes.
Proper toothbrushing is crucial for preventing dental caries and periodontal disease, yet adherence to correct techniques is often poor.
This gap necessitates innovative solutions for monitoring and improving brushing habits.

Purpose of the Study:

To develop and evaluate a fine-grained toothbrushing region recognition system.
To assess the efficacy of machine learning classifiers and inertial measurement units (IMUs) for real-time brushing analysis.
To enhance the reliability of oral hygiene monitoring through advanced signal processing.

Main Methods:

Utilized six machine learning classifiers and two IMUs (toothbrush holder and wrist-mounted).
Developed a hierarchical approach to identify brushing activities and recognize specific oral regions.
Implemented post-processing strategies including contextual smoothing and majority voting for improved accuracy.

Main Results:

The Random Forest classifier achieved the highest accuracy (96.13%), sensitivity (96.10%), precision (95.51%), and F1-score (95.60%).
The proposed system effectively distinguished between brushing and transition activities.
Demonstrated feasibility for detailed toothbrushing region recognition.

Conclusions:

The developed approach provides effective and feasible fine-grained toothbrushing region recognition.
This technology holds potential for improving toothbrushing monitoring and promoting better oral hygiene.
Accurate recognition of brushing habits can contribute to the prevention of oral diseases.