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Respiratory rate (RR) is a clinical metric used to assess overall health andphysical fitness. An individual's RR can change from their baseline due tochronic illness symptoms (e.g., asthma, congestive heart failure), acuteillness (e.g., breathlessness due to infection), and over the course of the daydue to physical exhaustion during heightened exertion. Remote estimation of RRcan offer a cost-effective method to track disease progression andcardio-respiratory fitness over time. This work investigates a model-drivenapproach to estimate RR from short audio segments obtained after physicalexertion in healthy adults. Data was collected from 21 individuals usingmicrophone-enabled, near-field headphones before, during, and after strenuousexercise. RR was manually annotated by counting perceived inhalations andexhalations. A multi-task Long-Short Term Memory (LSTM) network withconvolutional layers was implemented to process mel-filterbank energies,estimate RR in varying background noise conditions, and predict heavybreathing, indicated by an RR of more than 25 breaths per minute. Themulti-task model performs both classification and regression tasks andleverages a mixture of loss functions. It was observed that RR can be estimatedwith a concordance correlation coefficient (CCC) of 0.76 and a mean squarederror (MSE) of 0.2, demonstrating that audio can be a viable signal forapproximating RR.

Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones