Remote Respiration Monitoring of Moving Person Using Radio Signals

Abstract

Non-contact respiration rate measurement (nRRM), which aims to monitor one’s breathing status without any contact with the skin, can be utilized in various remote applications (e.g., telehealth or emergency detection). The existing nRRM approaches mainly analyze fine details from videos to extract minute respiration signals; however, they have practical limitations in that the head or body of a subject must be quasi-stationary. In this study, we examine the task of estimating the respiration signal of a non-stationary subject (a person with large body movements or even walking around) based on radio signals. The key idea is that the received radio signals retain both the reflections from human global motion (GM) and respiration in a mixed form, while preserving the GM-only components at the same time. During training, our model leverages a novel multi-task adversarial learning (MTAL) framework to capture the mapping from radio signals to respiration while excluding the GM components in a self-supervised manner. We test the proposed model based on the newly collected and released datasets under real-world conditions. This study is the first realization of the nRRM task for moving/occluded scenarios, and also outperforms the state-of-the-art baselines even when the person sits still.