# Earables for Detection of Bruxism: a Feasibility Study

arXiv: https://arxiv.org/abs/2108.04144 · HTML: https://mlsanity.com/paper/2108.04144/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [OESense: Employing Occlusion Effect for In-ear Human Sensing](https://mlsanity.com/paper/2106.08607/) (2021)

## Built on by (most cited first, 1 of 1)

- [UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers](https://mlsanity.com/paper/2402.09264/) (2024)

## Abstract (arXiv)

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.