Earables for Detection of Bruxism: a Feasibility Study

TL;DR

This study investigates the potential of using ear-worn devices with inertial sensors and machine learning to unobtrusively detect bruxism events like teeth grinding and clenching in real-world settings.

Contribution

It demonstrates the feasibility of earables combined with gyroscope data and machine learning for early, unobtrusive bruxism detection in natural environments.

Findings

Gyroscope data yields high accuracy in controlled settings.

Model accuracy remains acceptable in in-the-wild environments.

Earables can potentially enable early bruxism diagnosis.

Abstract

Bruxism is a disorder characterised by teeth grinding and clenching, and many bruxism sufferers are not aware of this disorder until their dental health professional notices permanent teeth wear. Stress and anxiety are often listed among contributing factors impacting bruxism exacerbation, which may explain why the COVID-19 pandemic gave rise to a bruxism epidemic. It is essential to develop tools allowing for the early diagnosis of bruxism in an unobtrusive manner. This work explores the feasibility of detecting bruxism-related events using earables in a mimicked in-the-wild setting. Using inertial measurement unit for data collection, we utilise traditional machine learning for teeth grinding and clenching detection. We observe superior performance of models based on gyroscope data, achieving an 88% and 66% accuracy on grinding and clenching activities, respectively, in a controlled environment, and 76% and 73% on grinding and clenching, respectively, in an in-the-wild environment.