Earables for Detection of Bruxism: a Feasibility Study
This work addresses the need for early, unobtrusive diagnosis of bruxism, a disorder exacerbated by stress and often unnoticed until dental damage occurs, though it is incremental as it applies existing methods to a new application.
The study tackled the problem of detecting bruxism (teeth grinding and clenching) by exploring the feasibility of using earables with inertial measurement units and traditional machine learning, achieving accuracies of up to 88% for grinding and 73% for clenching in controlled and in-the-wild environments.
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.