Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems

Abstract

Wireless communications systems provide channel state information (CSI), which can be used to characterize the physical propagation environment. The small physiological motion of human subjects in that environment, such as that associated with respiration, can modulate the CSI, thus allowing wireless physiological sensing through which a communications system detects and monitors cardiopulmonary motion. NextG millimeter-wave communications systems present even greater opportunities for wireless physiological sensing due to the advantages of small wavelength and high directionality. But challenges also arise due to the multipath effect and phase aliasing caused by larger-than-wavelength motion displacement. This work introduces a comprehensive mathematical CSI model to accurately characterize physiological motion captured by the amplitude and phase of the CSI of a millimeter-wave communications system. The model allows for the interpretation of intricate CSI pattern variations to avoid aliasing error and has been validated with experiments involving both parametric measures conducted with a robotic mover and respiration rate measurements for human subjects. In all cases, rate measurements could be consistently resolved within 10%, or 0.02 Hz, demonstrating the potential for incorporating physiological sensing in NextG millimeter-wave communications systems.