Indirect Estimation of Breathing Rate Using Wearable Devices

Wearable sensors can be exploited for the indirect estimation of physiological parameters, such as breathing rate (BR). Indeed, BR is a significative quantity for both general health status monitoring and diagnostic purposes; however, standard methods for its assessment are often uncomfortable and mainly used for punctual (or brief, anyway) measurements. This article aims to perform an uncertainty analysis of BR indirect estimation made starting from electrocardiographic signals gathered through wearable sensors, namely, a cardiac belt (Zephyr BioHarness 3.0) and a smartwatch (Samsung Galaxy Watch3). Three different estimation methods were employed, considering respiratory sinus arrhythmia (RSA), signal amplitude modulation (AM), and machine learning (ML)-based techniques. Finally, the Monte Carlo simulation method was exploited for the measurement uncertainty estimation, including both sensors (hardware) and algorithms (software) contributions in the measurement chain. The results show that both the considered sensors are quite accurate (almost null bias) and precise (±[3, 5] bpm, depending on the estimation method) in the estimation of BR with the three different estimation algorithms. A slightly higher precision is obtained for the cardiac belt (a reduced 95% confidence interval is reported, with a maximum reduction of 4 bpm depending on the estimation algorithm), whose results are also more strongly correlated to the reference ones (Pearson’s correlation coefficient
$\ge 0.75$
in all the three methods). The Monte Carlo simulation evidenced that the ML-based method is the most robust with respect to the sensors’ uncertainty (with no differences in the output uncertainty with respect to the sensors’ uncertainty in input); moreover, the higher precision of the cardiac belt with respect to the smartwatch was confirmed (−1 bpm in the output uncertainty) if RSA- and AM-based methods are considered.