BreathTrack: Detecting Regular Breathing Phases from UnannotatedAcoustic Data Captured by a Smartphone

Passive and continuous monitoring of breathing biomarkers is vital for assessing well-being and detecting abnormalities in breathing patterns. In this paper, we present a novel method to detect breathing phases during regular breathing towards passive monitoring of natural breathing using acoustic sensors embedded in smartphones. Our model eliminates the need for breathing sound annotation by transferring knowledge from inertial sensor to acoustic sensor and by fusing signal processing techniques with deep learning methods. Our study with 131 subjects including healthy subjects and pulmonary patients shows that our model can detect breathing phases with 77.33% accuracy using acoustic sensors which enables novel and fine-grained breathing biomarkers such as inhalation exhalation ratio, fractional inspiratory time including commonly known vital sign called breathing rate. We further show that our algorithm can estimate fractional inspiratory time with92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the commonly known breathing rate with 91.74% accuracy. We further present the respiratory patient detection model as an example application of breathing phase detection and novel biomarker extraction. We show that fractional inspiratory time is significantly correlated with patient severity and our model can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first work to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory well-being using a smartphone.