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• W3048640785 abstract "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 wellbeing 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, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>BreathListener</i> , 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, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>BreathListener</i> eliminates interference from driving environments in ESD signals utilizing background subtraction and variational mode decomposition (VMD). 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 ten drivers in real driving environments show that <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>BreathListener</i> can accurately capture breathing patterns of drivers in driving environments." @default.
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• W3048640785 date "2022-03-01" @default.
• W3048640785 modified "2023-10-17" @default.
• W3048640785 title "Leveraging Acoustic Signals for Fine-Grained Breathing Monitoring in Driving Environments" @default.
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• W3048640785 doi "https://doi.org/10.1109/tmc.2020.3015828" @default.
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