Abstract
Bruxism is associated with multiple health issues and affects millions of people worldwide. To enable effective interventions, precise, easy-to-use and unobtrusive detection systems are required. Unfortunately, especially for daytime bruxism, such systems still rely on electrodes placed on the face, recording diaries, and manual algorithm tuning. In this work, we present a novel approach for bruxing event detection using comfortable and inconspicuous around-the-ear sensors (cEEGrids) as a form of distal EMG-based measurement. Using Random Forest classifiers on laboratory experiment data, promising F1-scores (up to 0.9) are found for the detection of bruxing events in contrast to a variety of other facial muscle activity events. Thereby, a promising new alternative for feasible awake bruxism detection is demonstrated.
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Knierim, M.T., Schemmer, M., Woehler, D. (2021). Detecting Daytime Bruxism Through Convenient and Wearable Around-the-Ear Electrodes. In: Ahram, T.Z., Falcão, C.S. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2021. Lecture Notes in Networks and Systems, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-030-80091-8_4
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DOI: https://doi.org/10.1007/978-3-030-80091-8_4
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