MOUTH BREATHING DETECTION USING AUDIO CAPTURED THROUGH EARBUDS

Mouth breathing has a significant adverse effect on people. For example, mouth breathing has been associated with sleep-related disorders and dental problems. Detecting mouth breathing in the daily environment during resting activities could be helpful for early intervention and reversing the negative impact. However, in previous works, mouth breathing detection in the everyday environment has not sufficiently been explored. This work presents a machine learning approach to detect mouth breathing using the audio captured by commercially available earbuds. Earbuds are becoming famous for health monitoring, and they could provide a more convenient method for detecting mouth breathing without requiring user attention. We conducted a data collection study with 30 participants to train the audio-based classifier. Our results suggest that a convolutional neural network based model can detect mouth breathing with 80.4\% accuracy.