Quantifying the Physical Separability of RF-Based Multi-Person Respiration Monitoring via SINR

Recent years have witnessed a growing interest in contact-free respiration monitoring leveraging radio-frequency (RF) technologies. However, the proposed solutions mostly consider single-person scenarios, whereas a few multi-person monitoring proposals simply apply blind source separation to handle inter-person interference, without drawing a clear line between physical and algorithmic separability. In this paper, we set out to answer: under what condition(s) one may physically separate multiple respiration signals sensed by diversified RF technologies? Drawing inspiration from conventional signal processing, we propose respiration-to-interference-plus-noise ratio (RINR) as a novel metric, taking into account the impact from both background noise and various interfering sources. Instead of attenuation in Euclidean distance, RINR has to be evaluated upon range/angle bins where physical separation actually take place. As signal attenuation has never been modeled in this manner, we rise to this challenge by levering a deep learning model to fit a spread function upon range/angle bins. The resulting RINR model allows us to concretely indicate the limit of physical separability of RF-based multi-person respiration monitoring. Our extensive experiments firmly validate the RINR model, thus evidently demonstrating the benefits of employing RINR model as a guideline for conducting respiration monitoring with different RF technologies.