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mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors.

Sayma Akther¹, Nazir Saleheen¹, Mithun Saha¹

¹University of Memphis.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

|March 21, 2022

Summary

This study introduces the mTeeth model using wrist sensors to identify brushed teeth surfaces. It accurately detects brushing strokes, improving oral hygiene monitoring and disease prevention.

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Area of Science:

Biomedical Engineering
Machine Learning
Oral Health Technology

Background:

Inadequate teeth surface coverage during brushing increases oral disease risk.
Accurate monitoring of brushing habits is crucial for preventive oral care.
Existing methods lack precision in real-world, free-living conditions.

Purpose of the Study:

To develop a model (mTeeth) for detecting brushed teeth surfaces using wrist-worn inertial sensors.
To introduce a lightweight method for identifying brushing strokes and surface transitions.
To improve the accuracy of automated oral hygiene monitoring.

Main Methods:

Proposed the mTeeth model utilizing wrist-worn inertial sensors for teeth surface detection.
Developed a micro-event detection method for brushing strokes to identify surface transitions.

Keywords:

brushing detection flossing detection hand-to-mouth gestures mHealth

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Employed a Bayesian Ensemble classifier leveraging teeth surface hierarchy and transition patterns.

Annotated a dataset with 10,230 brushing instances across 114 episodes for training and testing.

Main Results:

The mTeeth model effectively detects teeth surfaces being brushed in a natural environment.
The brushing stroke detection method accurately captures millisecond-scale transitions between surfaces.
The Bayesian Ensemble method demonstrated robust performance despite significant user variability.
Evaluated the impact of between-person and within-person variability on model performance.

Conclusions:

The mTeeth model offers a promising approach for monitoring brushing coverage using inertial sensors.
Accurate detection of brushing strokes and surfaces can enhance oral hygiene practices.
This technology has the potential to aid in the prevention of oral diseases through improved self-care.