Tracking of nightly oral parafunctional activities, such as grinding and clenching, is useful to monitor sleep bruxism. Although polysomnography remains the gold standard for overnight observations, home-based long-term recordings are wished, to ensure time-distributed data and better patient compliance. Technologies based on inertial measurement unit (IMU) sensors allow the production of compact, and easy-to-use sensing instruments. This study aimed to develop a proof of concept using IMUs to capture mandibular motion for sleep bruxism assessment and to evaluate the accuracy of machine learning algorithms in classifying motions. A mobile recording setup was developed, incorporating an IMU equipped with tri-axial accelerometer, gyroscope, and magnetometer. A set of 21 in-vivo recordings from three individuals containing grinding and opening/closing mandibular movements was collected for ML algorithm training. The data was manually labeled and divided into train and test data (80/20). Several models were trained to classify the mandibular motion. Overall, the trained models were capable of correctly classifying up to 96% of the test data. The best results were obtained when data from all three sensors were used simultaneously. The results indicate that IMU sensors are a valuable option to assess mandibular motion for a small-scale wearable device.
Schlaepfer et al. (Mon,) studied this question.