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Respiratory rate (RR) serves as an indicator of various medical conditions,such as cardiovascular diseases and sleep disorders. These RR estimationmethods were mostly designed for finger-based PPG collected from subjects instationary situations (e.g., in hospitals). In contrast to finger-based PPGsignals, wrist-based PPG are more susceptible to noise, particularly in theirlow frequency range, which includes respiratory information. Therefore, theexisting methods struggle to accurately extract RR when PPG data are collectedfrom wrist area under free-living conditions. The increasing popularity ofsmartwatches, equipped with various sensors including PPG, has prompted theneed for a robust RR estimation method. In this paper, we propose aconvolutional neural network-based approach to extract RR from PPG,accelerometer, and gyroscope signals captured via smartwatches. Our method,including a dilated residual inception module and 1D convolutions, extract thetemporal information from the signals, enabling RR estimation. Our method istrained and tested using data collected from 36 subjects under free-livingconditions for one day using Samsung Gear Sport watches. For evaluation, wecompare the proposed method with four state-of-the-art RR estimation methods.The RR estimates are compared with RR references obtained from a chest-banddevice. The results show that our method outperforms the existing methods withthe Mean-Absolute-Error and Root-Mean-Square-Error of 1.85 and 2.34, while thebest results obtained by the other methods are 2.41 and 3.29, respectively.Moreover, compared to the other methods, the absolute error distribution of ourmethod was narrow (with the lowest median), indicating a higher level ofagreement between the estimated and reference RR values.

Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU