Wearable Regionally Trained AI-Enabled Bruxism-Detection System

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...

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Main Authors: Anusha Ishtiaq, Jahanzeb Gul, Zia Mohy Ud Din, Azhar Imran, Khalil El Hindi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10848082/
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author Anusha Ishtiaq
Jahanzeb Gul
Zia Mohy Ud Din
Azhar Imran
Khalil El Hindi
author_facet Anusha Ishtiaq
Jahanzeb Gul
Zia Mohy Ud Din
Azhar Imran
Khalil El Hindi
author_sort Anusha Ishtiaq
collection DOAJ
description 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.
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id doaj-art-e04f9ff66ea94939ba64a1d5a3b2868f
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publishDate 2025-01-01
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spelling doaj-art-e04f9ff66ea94939ba64a1d5a3b2868f2025-01-28T00:01:16ZengIEEEIEEE Access2169-35362025-01-0113155031552810.1109/ACCESS.2025.353236010848082Wearable Regionally Trained AI-Enabled Bruxism-Detection SystemAnusha Ishtiaq0Jahanzeb Gul1https://orcid.org/0000-0002-1230-2546Zia Mohy Ud Din2https://orcid.org/0000-0001-5756-7284Azhar Imran3https://orcid.org/0000-0003-3598-2780Khalil El Hindi4https://orcid.org/0000-0003-2457-9961Department of Biomedical Engineering, Air University, Islamabad, PakistanDepartment of Electronic Engineering, Maynooth University, Maynooth, IrelandDepartment of Biomedical Engineering, Air University, Islamabad, PakistanDepartment of Creative Technologies, Faculty of Computing and AI, Air University, Islamabad, PakistanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaSleep 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.https://ieeexplore.ieee.org/document/10848082/Bruxismclassificationdeep learningmasseter muscletemporalis muscle
spellingShingle Anusha Ishtiaq
Jahanzeb Gul
Zia Mohy Ud Din
Azhar Imran
Khalil El Hindi
Wearable Regionally Trained AI-Enabled Bruxism-Detection System
IEEE Access
Bruxism
classification
deep learning
masseter muscle
temporalis muscle
title Wearable Regionally Trained AI-Enabled Bruxism-Detection System
title_full Wearable Regionally Trained AI-Enabled Bruxism-Detection System
title_fullStr Wearable Regionally Trained AI-Enabled Bruxism-Detection System
title_full_unstemmed Wearable Regionally Trained AI-Enabled Bruxism-Detection System
title_short Wearable Regionally Trained AI-Enabled Bruxism-Detection System
title_sort wearable regionally trained ai enabled bruxism detection system
topic Bruxism
classification
deep learning
masseter muscle
temporalis muscle
url https://ieeexplore.ieee.org/document/10848082/
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AT ziamohyuddin wearableregionallytrainedaienabledbruxismdetectionsystem
AT azharimran wearableregionallytrainedaienabledbruxismdetectionsystem
AT khalilelhindi wearableregionallytrainedaienabledbruxismdetectionsystem