BreathListener: Fine-grained Breathing Monitoring in Driving Environments Utilizing Acoustic Signals

Given the increasing amount of time people spent on driving, the physical and mental health of drivers is essential to road safety. Breathing patterns are critical indicators of the well-being of drivers on the road. Existing studies on breathing monitoring require active user participation of wearing special sensors or relatively quiet environments during sleep, which are hardly applicable to noisy driving environments. In this work, we propose a fine-grained breathing monitoring system, BreathListener, which leverages audio devices on smartphones to estimate the fine-grained breathing waveform in driving environments. By investigating the data collected from real driving environments, we find that Energy Spectrum Density (ESD) of acoustic signals can be utilized to capture breathing procedures in driving environments. To extract breathing pattern in ESD signals, BreathListener eliminates interference from driving environments in ESD signals utilizing background subtraction and Ensemble Empirical Mode Decomposition (EEMD). After that, the extracted breathing pattern is transformed into Hilbert spectrum, and we further design a deep learning architecture based on Generative Adversarial Network (GAN) to generate fine-grained breathing waveform from the Hilbert spectrum of extracted breathing patterns in ESD signals. Experiments with 10 drivers in real driving environments show that BreathListener can accurately capture breathing patterns of drivers in driving environments.