Wearable Regionally Trained AI-Enabled Bruxism-Detection System - Anusha Ishtiaq; Jahanzeb Gul; Zia Mohy Ud Din; Azhar Imran; Khalil El Hindi (IEEE Access)
Sleep Bruxism (SB) and Awake Bruxism (AB) can cause severe discomfort, exhaustion, and problems with day-to-day functioning, including poor sleep and bad performance at work. This emphasizes the significance of early identification and treatment of bruxism. To date, some tools like mouthpieces have been designed for teeth protection. However, they are not user-friendly due to their internal placement in the mouth. Bruxers require a gadget that not only identifies and continually monitors their bruxism activity, but also alerts them. In this study, a wearable EMG-based device has been designed to monitor and detect jaw clenching in the supine position using EMG of the two facial muscles, Temporalis and Masseter. This study purposely found which muscle varies most with bruxism activity. The EMG signals’ data of 30 regional subjects, with 5 trials each, have been acquired and pre-processed using filters and three data oversampling techniques, SMOTE, SMOTE-ENN, and ADSYN. The augmented data has been trained, validated, and tested on six machine-learning classifiers and three deep-learning models. The Recurrent Neural Network provided the highest accuracy 0.99 and a recall value 0.98 for the temporalis muscle dataset. The other eight classifiers have provided accuracies in descending order such as Convolutional Neural Network, Long Short-Term Memory, k-Nearest Neighbors, and Decision Tree 0.98; Logistic Regression 0.96; Support Vector Machine 0.97, and Naïve Bayes 0.89, respectively. The module has been tested on several participants, and bruxism is identified when they do jaw clenching or teeth grinding. In the future, the size of the gadget could be miniaturized to ensure the users’ comfort level.