BPqu: Breath Phase Quantifier

Endurance running is a popular activity due to its accessibility. However, participation is sometimes prevented by individuals experiencing respiratory problems. Monitoring breathing with body area networks can tackle these issues by tracking respiration during exercise and providing immediate, guiding feedback. Common breathing guidance systems rely on observational data from past breath cycles and consequently inherit disruptively lagging guidance interventions if breathing pattern suddenly change. Therefore, this work introduces BPqu, a deep learning algorithm for real-time breath phase estimation on edge devices that enhances new breathing guidance opportunities in running by predicting the continuous breath phase with zero latency. BPqu predicts breathing within the next running step and detects hale change events, determines inhalation and exhalation phases, and quantifies the concurrent continuous breath phase with performance metrics of 93.1%, 85.6%, and 81.5%, respectively, with zero delay. BPqu is designed to be deployed in a textile wearable breathing and stride sensor and will be applied to enhance intuitive,  non-distracting breathing guidance in running, reducing respiratory distress and increasing running enjoyment, thereby promoting human well-being.