Multirange Breathing Rate Estimation With Deep Learning Using FMCW Radar

Frequency-modulated continuous wave (FMCW) radar is a promising technology for noninvasive monitoring of vital signs, such as breathing rate (BR). A key challenge lies in selecting the correct range bin corresponding to the human target, particularly in dynamic environments and resource-constrained systems. This article introduces a resource-efficient deep learning (DL) method for multirange BR estimation that significantly enhances accuracy while maintaining low computational and memory requirements, making it suitable for embedded systems. We benchmark our method against two alternative single-bin approaches: energy-based and spatial phase coherency (SPC) range bin selection, both integrated with DL for BR estimation. Performance is evaluated on an experimental dataset comprising recordings from 25 subjects, with 17 used for training and eight for testing. Recordings were conducted under diverse conditions, spanning various distances, angles, sensor mounting heights, and static and dynamic background scenarios, including individuals walking in the background. Our method achieves a root-mean-square error (RMSE) of 1.88 breaths per minute (bpm) and a Spearman correlation coefficient of 0.83, outperforming state-of-the-art methods that typically report higher RMSEs on smaller datasets. With a minimal memory footprint of approximately 25 KB, the proposed approach is well-suited for resource-constrained embedded systems. This advancement underscores the potential of lightweight DL for reliable and accurate noninvasive vital sign monitoring in healthcare applications.