BreathIE: Estimating Breathing Inhale Exhale Ratio Using Motion Sensor Data from Consumer Earbuds

• Published in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
• Main Authors: Rashid, Nafiul, Rahman, Md Mahbubur, Ahmed, Tousif, Kuang, Jilong, Gao, Jun Alex
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04-06-2023
• Subjects: Biomarkers; Heuristic algorithms; Lung; Motion detection; Signal processing; Signal processing algorithms; Wearable computers
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49357.2023.10096084

Breathing Inhale/Exhale (IE) ratio is one of the critical breathing biomarkers for pulmonary patients and healthy individuals. It can indicate the severity of lung obstruction for chronic lung patients and help detect psycho-social stress for healthy individuals. With the advancement of wearable technologies, common consumer wearables such as smartwatches offer breathing rates. However, IE ratio measurement is not available in consumer wearable devices till today. In this paper, we present a novel algorithm, BreathIE, to estimate breathing rate and IE ratio using a low-power motion sensor embedded in consumer-grade earbuds. Moreover, our algorithm is adaptive and dynamically adjusts to the user's breathing habit by accommodating varying breathing durations at run time. We conducted a study with 30 participants, where both earbuds and a reference chestband device were used simultaneously. Experimental evaluation against the annotated reference data shows that our algorithm can estimate breathing rate with a mean absolute error (MAE) of 2.37 breaths per minute (BPM) and breathing IE ratio of 0.27 MAE while outperforming the state-of-the-art algorithms.