Breeze: Smartphone-based acoustic real-time detection of breathing phases for a gamified biofeedback breathing training

Abstract

Slow-paced biofeedback-guided breathing training has been shown to improve cardiac functioning and psychological wellbeing. Current training options, however, attract only a fraction of individuals and are limited in their scalability as they require dedicated biofeedback hardware. In this work, we present Breeze, a mobile application that uses a smartphone's microphone to continuously detect breathing phases, which then trigger a gamified biofeedback-guided breathing training. Circa 2.76 million breathing sounds from 43 subjects and control sounds were collected and labeled to train and test our breathing detection algorithm. We model breathing as inhalation-pause-exhalation-pause sequences and implement a phase-detection system with an attention-based LSTM model in conjunction with a CNN-based breath extraction module. A biofeedback-guided breathing training with Breeze takes place in real-time and achieves 75.5% accuracy in breathing phases detection. Breeze was also evaluated in a pilot study with 16 new subjects, which demonstrated that the majority of subjects prefer Breeze over a validated active control condition in its usefulness, enjoyment, control, and usage intentions. Breeze is also effective for strengthening users' cardiac functioning by increasing high-frequency heart rate variability. The results of our study suggest that Breeze could potentially be utilized in clinical and self-care activities.