Deep Audio Spectral Processing for Respiration Rate Estimation from Commodity Earbuds

Breathing rate is an important health biomarker and a vital indicator for health and fitness. With smart earbuds gaining popularity as a commodity device, recent works have demonstrated the potential for monitoring breathing rate using such earable devices. In this work, we use spectrograms from breathing cycle audio signals captured using earbuds as a spectral feature to train a deep convolutional neural network to infer respiration rate with high accuracy. Using novel earbud audio data collected from 30 subjects with both controlled breathing at a wide range (from 5 upto 45 breaths per minute), and uncontrolled natural breathing from 7-day home deployment, experimental results demonstrate that our model can estimate respiration rate with 0.77 MAE for controlled breathing and with 0.99 MAE for at-home natural breathing.