A Denoising Diffusion Probabilistic Model-Based Human Respiration Monitoring Method Using a UWB Radar

Abstract

Real-time respiratory monitoring when sleeping is crucial for sleep apnea, chronic obstructive pulmonary disease, sleep quality assessment, and other issues related to the tracking of human health status. With the advantages of easy deployment, no wearing burden, and low privacy disclosure, recent years have witnessed a growing interest in device-free respiration monitoring leveraging radio-frequency (RF) sensing. This paper proposes a denoising diffusion probabilistic model (DDPM)-based human respiration monitoring method using an ultra-wideband (UWB) radar, where the localization calculation of the target based on the respiration-motion energy ratio, maximum ratio combining (MRC), and principal component analysis (PCA) are included for data enhancement. Moreover, a real-time sleep respiration monitoring system has been designed and implemented, which is composed of a civilian UWB radar development board, a Raspberry Pi 3B, and a PC, and extensive experiments have been carried out to validate our proposed method. Compared to the commercial respiratory tapes, our method shows that the respiratory rate estimation accuracy and the cosine similarity of respiratory waveforms can reach up to 94% and 87.9%, respectively, rendering it can be considered a viable solution for contact-free respiration monitoring for health.