MetaPhys: Contactless Physiological Sensing of Multiple Subjects Using RIS-Based 4-D Radar

• Published in: IEEE internet of things journal Vol. 10; no. 14; pp. 12616 - 12626
• Main Authors: Li, Zhi, Jin, Tian, Guan, Dongfang, Xu, Hantao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 4-D radar; Beamforming; Biomedical monitoring; Body parts; contactless physiological sensing; Echoes; Electromagnetic radiation; Heart rate; Indoor environments; MetaPhys; Monitoring; New technology; Physiology; Radar; Radar antennas; Radar detection; Radar imaging; reconfigurable intelligent surface (RIS); Reflectors; Respiratory rate; Sensors; Signal monitoring; Signal to noise ratio
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3252587

Contactless physiological signal sensing is an emerging technology for routine health monitoring, ambient-assisted living, automotive, search and rescue, and public security. In this work, we propose MetaPhys, a reconfigurable intelligent surface (RIS)-based contactless physiological signal monitoring system that can simultaneously sense multiple persons and their respiratory and cardiac signals. The proposed system localizes the human targets by utilizing RIS to dynamically manipulate the electromagnetic waves in the environment for beamforming and 3-D radar imaging in the spatial dimension. Then, it extracts the respiration and cardiac signals from the sequential echoes in the temporal dimension. The high-resolution information in the four dimensions as "3-D space + 1-D time" makes it comprehensively perceive the targets and environment. The beamforming allows the radiated energy to focus on the thoraxes, which helps reduce interference and distortion caused by stationary reflectors (i.e., clutters, static body parts, and multipath), thereby improving the signal-to-noise ratio and enabling long-range measurements. We also verify the measured data in a real indoor environment, and the experimental results show that the proposed system can accurately monitor the physiological signals of multiple subjects. The root mean square errors of respiratory rate and heart rate at a distance of 3 m are 0.2 breaths per minute and 1.1 beats per minute, respectively.