Towards Motion-Aware Passive Resting Respiratory Rate Monitoring Using Earbuds


Breathing rate is an important vital sign and an indicator of overall health and fitness. Traditionally breathing is monitored using specialized devices such as chestband or spirometers. However, these are uncomfortable for everyday use. Recent works show the feasibility of estimating breathing rate using earbuds. However, non-breathing head motion is one of the biggest challenges for accurate breathing rate estimation using earbuds or other head-mounted devices such as smart-glass. In this paper, we propose an algorithm to estimate the breathing rate in presence of non-breathing head motion using inertial sensors embedded in commodity earbuds. Using the chestband as a reference device, we show that our algorithms can estimate breathing rate in resting positions with $\pm$ 2.63 breaths per minute (BPM) error. However, when the algorithms developed on data without head motion and applied to the data with head motion, the error significantly increases. Our head-motion handling algorithm proposed in this paper can improve the accuracy up to 30\% in the presence of non-breathing head motion. This paper can help make a big stride towards passive breathing monitoring in everyday life using commodity earbuds which are increasingly becoming popular nowadays.

Author: Mahbubur Rahman, Tousif Ahmed, Mohsin Ahmed, Ebrahim Nematihosseinabadi, Minh Dinh, Nathan Robert Folkman, Jilong Kuang, Jun Gao

Published: IEEE-EMBS International Conference on Biomedical and Health Informatics(BHI) and the Body Sensor Networks(BSN) Conferences

Date: Jul 27, 2021