Novel Continuous Respiratory Rate Monitoring Using an Armband Wearable Sensor

Abstract

Photoplethysmography (PPG) and accelerometer (ACC) are commonly integrated into wearable devices for continuous unobtrusive pulse rate and activity monitoring of individuals during daily life. However, obtaining continuous and clinically accurate respiratory rate measurements using such wearable sensors remains a challenge. This article presents a novel algorithm for estimation of respiration rate (RR) using an upper-arm worn wearable device by deriving multiple respiratory surrogate signals from PPG and ACC sensing. This RR algorithm is retrospectively evaluated on a controlled respiratory clinical testing dataset from 38 subjects with simultaneously recorded wearable sensor data and a standard capnography monitor as an RR reference. The proposed RR method shows great performance and robustness in determining RR measurements over a wide range of 4-59 brpm with an overall bias of -1.3 brpm, mean absolute error (MAE) of 2.7±1.6 brpm, and a meager outage of 0.3±1.2%, while a standard PPG Smart Fusion method produces a bias of -3.6 brpm, an MAE of 5.5±3.1 brpm, and an outage of 0.7±2.5% for direct comparison. In addition, the proposed algorithm showed no significant differences (p=0.63) in accurately determining RR values in subjects with darker skin tones, while the RR performance of the PPG Smart Fusion method is significantly (P<0.001) affected by the darker skin pigmentation. This study demonstrates a highly accurate RR algorithm for unobtrusive continuous RR monitoring using an armband wearable device.