Respiratory rate (RR) is an important vital sign to monitor outside the clinic, particularly during physiological challenges such as exercise; unfortunately, ambulatory measurement devices for RR are typically obtrusive and inaccurate. The objective of this work is to allow for accurate and robust RR monitoring with a convenient and small chest-worn wearable patch during walking and exercise recovery periods.

To estimate RR from the wearable patch, respiratory signals were first extracted from electrocardiogram (ECG), photoplethysmogram (PPG), and seismocardiogram (SCG) signals. The optimal channel in each signal was adaptively selected using the respiratory quality index based on fast Fourier transform (RQIFFT). Next, we proposed modality attentive (MA) fusion—which merged spectral–temporal information from different modalities—to address motion artifacts during walking. The fused output was subsequently denoised using a U-Net-based deep learning model and used for final estimation. A dataset of = 17 subjects was collected to validate the RR estimated during three types of activities: stationary activities, walking (including 6-minute walk test), and running.

Combining and denoising ECG and PPG data using MA fusion and the U-Net achieved the lowest mean absolute error (MAE) (2.21 breaths per minute [brpm]) during walking. After rejecting a small portion of the data (coverage = 84.43%) using RQIFFT, this error was further reduced to 1.59 brpm, which was comparable to the state-of-the-art methods.

Applying adaptive channel selection, MA fusion, and U-Net denoising achieved accurate RR estimation from a small chest-worn wearable patch.

This work can enable cardiopulmonary monitoring applications in less controlled settings.