Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones

TL;DR

This study develops a multi-task LSTM model to estimate respiratory rate from breath audio recorded via wearable microphones, demonstrating promising accuracy in noisy conditions for health monitoring.

Contribution

It introduces a novel multi-task deep learning approach to estimate RR from short audio segments, including heavy breathing detection, in a wearable setting.

Findings

Achieved a CCC of 0.76 in RR estimation

Estimated RR with an MSE of 0.2

Effective in noisy environments

Abstract

Respiratory rate (RR) is a clinical metric used to assess overall health and physical fitness. An individual's RR can change from their baseline due to chronic illness symptoms (e.g., asthma, congestive heart failure), acute illness (e.g., breathlessness due to infection), and over the course of the day due to physical exhaustion during heightened exertion. Remote estimation of RR can offer a cost-effective method to track disease progression and cardio-respiratory fitness over time. This work investigates a model-driven approach to estimate RR from short audio segments obtained after physical exertion in healthy adults. Data was collected from 21 individuals using microphone-enabled, near-field headphones before, during, and after strenuous exercise. RR was manually annotated by counting perceived inhalations and exhalations. A multi-task Long-Short Term Memory (LSTM) network with convolutional layers was implemented to process mel-filterbank energies, estimate RR in varying background noise conditions, and predict heavy breathing, indicated by an RR of more than 25 breaths per minute. The multi-task model performs both classification and regression tasks and leverages a mixture of loss functions. It was observed that RR can be estimated with a concordance correlation coefficient (CCC) of 0.76 and a mean squared error (MSE) of 0.2, demonstrating that audio can be a viable signal for approximating RR.