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<p>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.</p>

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A new approach for the field detection of sleep bruxism based on inertial sensor data and machine learning classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月5日

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  • Barbara Schlaepfer,
  • Julian Langer,
  • Stefan Erni,
  • Vera Colombo

摘要

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.