MindfulBuddy: Extracting Comprehensive Breathing Biomarkers for Breathing Exercise Biofeedback Using Earbud Motion Sensors

Slow-paced deep breathing exercises have many health benefits, including stress management, lowering blood pressure, pain management, and controlling pulmonary conditions. While biofeedback can significantly improve the efficacy of breathing exercises, existing approaches support limited biomarkers, such as breathing rate, for specific breathing exercises (e.g., equal-phase breathing) in particular conditions without considering the breath-holding phase or variation in device orientation. Therefore, there needs to be a more convenient and robust approach that can generate and deliver comprehensive digital breathing biomarkers to facilitate biofeedback for various types of breathing exercises. In this paper, we present a system with lightweight algorithms to passively track mindful breathing in real-time using lower-power earbud motion sensors to extract fine-grained comprehensive breathing biomarkers for generating biofeedback on users’ breathing exercises. We utilize the earbud’s motion sensor data to detect non-breathing head motion and develop an extensive set of breathing markers, including breathing phases, breathing depth, breathing rate, breathing symmetry, and breath-holding. Such a comprehensive set of biomarkers can enable engaging user experience and effective mindful breathing exercises towards better stress management and overall mental well-being. Moreover, we develop a physiologically informed, novel earbud orientation handling algorithm that makes our biomarkers more resilient to ear canal shape and size. Finally, we showcase potential use-cases based on the breathing biomarkers derived from our algorithms to provide biofeedback on user’s overall breathing performance.