Bruxism Recognition via Wireless Signal

TL;DR

This paper presents a non-invasive, privacy-preserving bruxism detection system using millimeter-wave radar, achieving high accuracy and offering a promising alternative to traditional methods.

Contribution

It introduces a contactless bruxism recognition approach based on radar signal analysis and machine learning, addressing privacy and comfort issues of existing techniques.

Findings

Achieved 96.1% accuracy in bruxism detection

Identified 11 key features from radar signals

Validated radar's effectiveness for SB recognition

Abstract

Bruxism is an oromandibular movement disorder involving teeth grinding and clenching, which severely impairs sleep quality and dental health. However, its diagnosis remains challenging, as existing methods often cause discomfort or compromise user privacy. To address these limitations, we establish a contactless bruxism recognition system based on millimeter-wave radar. First, we analyzed the potential impact of the movement patterns of teeth grinding on radar echo signals. Based on this analysis, 11 features were extracted. Subsequently, using these features, we performed classification with a Random Forest model on the dataset constructed via millimeter-wave radar. Experimental results demonstrate that the proposed method achieves an accuracy of 96.1% on the test set, with precision, recall, and F1-score all remaining at a relatively high level. This study validates the effectiveness of millimeter-wave radar for SB recognition, providing a non-invasive and privacy-friendly alternative to existing recognition techniques. Future research will focus on enhancing the robustness of the method across diverse populations and environments, as well as striving to mitigate the interference of other facial micro-movements on teeth grinding recognition.