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BreatheBuddy: Tracking Real-time Breathing Exercises for Automated Bio-feedback Using Commodity Earbuds


Breathing exercises reduce stress and improve overall mental well-being. There are various types of breathing exercises. Performing the exercises correctly may give the best outcome and doing it in wrong ways can sometimes have adverse effect. Providing real-time biofeedback can greatly improve the user experience in doing the right exercises in the right ways. In this paper, we present methods to passively track breathing biomarkers in real-time using wireless commodity earbuds and generate feedback on users breathing performance. We use the earbuds low-power accelerometer to generate a comprehensive set of breathing biomarkers including breathing phase, breathing rate, depth of breathing, and breathing symmetry. We have conducted studies where the subjects performed different types of guided breathing exercises while wearing the earbuds. Our algorithms detect breathing phases with ~88.88\% accuracy and estimate breathing rate with ~95\% accuracy. We further show that our algorithms can be used to generate biofeedback towards designing engaging smartphones user interactions that facilitate users to accurately perform various breathing exercises.

Author: Mahbubur Rahman, Tousif Ahmed, Mohsin Ahmed, Retiree, Ebrahim Nematihosseinabadi, Jilong Kuang, Alex Gao

Published: Mobile HCI

Date: Oct 1, 2022