  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.
