An Asymmetrical Acoustic Field Detection System for Daily Tooth Brushing Monitoring

In this paper, we propose a tooth brushing monitoring system based on acoustic inputs through an asymmetrical sound-field detector. This detector consists of a throat microphone and a Bluetooth earphone equipped on the user's neck and ear, respectively. This system can capture unique acoustic signals generated by the movement of the toothbrush on the surfaces of teeth via the detector. The throat microphone captures the brushing sound travelling through gums, bones, and muscles, which forms unique patterns with less attenuation than the sound travelling through the air. The Bluetooth earphone captures the brushing sound through the air. The tooth surface is divided into 16 parts for detection. By adopting machine learning models with the input of acoustic features from both time and frequency domains, we build a high accuracy detector to distinguish the brushing events happened at each of the 16 parts of the tooth surface. We employ Support Vector Machine (SVM), Hidden Markov Model (HMM), K-Means, C4.5 and Random Forest (RF) to evaluate the performance of our detection system. Experiments show that the RF model performs the best and achieves an average accuracy of 85.69\%. Based on the pre- trained model, we develop an Android-based APP to monitor the user's daily tooth brushing time and help the user form a good habit of tooth brushing.