ViMo: Multiperson Vital Sign Monitoring Using Commodity Millimeter-Wave Radio

• Published in: IEEE internet of things journal Vol. 8; no. 3; pp. 1294 - 1307
• Main Authors: Wang, Fengyu, Zhang, Feng, Wu, Chenshu, Wang, Beibei, Liu, K. J. Ray
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Calibration; Dynamic programming; Estimation; Heart beat; Heart rate; Heart rate (HR) estimation; Human motion; Impulse response; Millimeter waves; Monitoring; Noise measurement; Object recognition; Remote monitoring; Remote sensing; Respiration; respiration signal elimination; smoothing spline; Static objects; Telemedicine; Wireless communication; Wireless fidelity; wireless sensing; Wireless sensor networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3004046

The continuous development of 802.11ad technology provides new opportunities in wireless sensing. In this work, we propose ViMo, a calibration-free remote vital sign monitoring system that can detect stationary/nonstationary users and estimate the respiration rates (RRs) as well as heart rates (HRs) built upon a commercial 60-GHz WiFi. The design of ViMo consists of two key components. First, we design an adaptive object detector that can identify static objects, stationary human subjects, and human in motion without any calibration. Second, we devise a robust HR estimator, which eliminates the respiration signal from the phase of the channel impulse response (CIR) to remove the interference of the harmonics from breathing and adopts dynamic programming (DP) to resist the random measurement noise. The influence of different settings, including the distance between a human and the device, user orientation and incidental angle, blockage material, body movement, and conditions of multiuser separation is investigated by extensive experiments. The experimental results show that ViMo monitors user's vital signs accurately, with a median error of 0.19 and 0.92 breaths per minute (BPM), respectively, for RR and HR estimation.