Robust Respiratory Rate Monitoring Using Smartwatch Photoplethysmography

Abstract

Respiratory rate (RR) is of great value in health care, especially when it can be continuously monitored using wearable devices in daily life. Recent works employ photoplethysmography (PPG) on smartwatch for continuous respiration monitoring, based on a certain medical discovery called respiratory sinus arrhythmia (RSA), which describes the relationship between respiratory and heart rate. However, we find that these works fall short of robustness. In particular, the respiratory estimation accuracy drops significantly when people breathe faster (e.g., after sports). We further identify the root reason that the RSA gradually weakens as the RR increases. In this article, we propose BreathAnalyzer, which can estimate RR accurately even at high RRs. To achieve this, BreathAnalyzer boosts the weakened RSA and also handles the motion artifacts, by integrating features from multiple domains, i.e., frequency, time, and nonlinear Poincare domain, instead of using the single spectrum or raw signal in previous studies. Moreover, BreathAnalyzer custom-designs a tree-based learning model, which fits multidomain features, while considering limitations of smartwatch. We implement BreathAnalyzer prototype on COTS smartwatch, and extensive evaluation demonstrates that BreathAnalyzer outperforms the state-of-the-art approaches, with accuracy improvement by 35.37%–80.42% across a variety of practical scenarios including high RRs.