RMVS: Remote Monitoring of Vital Signs with mm-Wave Radar

Zhanjun Hao, Hao Yan, Xiao-chao Dang, Zhongyu Ma, Wenze Ke, Peng Jin
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Abstract

Indoor vital signs monitoring is beneficial for people's healthy life. However, the monitoring distance limits most existing mm-wave radar-based vital signs monitoring methods. Enhancing and analyzing the echo signal helps to improve the monitoring distance. In this work, we propose remote monitoring of vital signs with the mm-wave radar (RMVS) method to achieve long-range vital signs monitoring of indoor personnel. RMVS fully uses multiple antennas to characterize the reflected signal from the chest cavity. By overlaying the signals, the dynamic signal of vital signs is enhanced, and the ambient static clutter is suppressed. RMVS constructs the mapping relationship between distance and micro-Doppler by overlaying the Doppler information at different distances. It solves the problem of low accuracy of thoracic localization in the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) paths by the traditional radar respiratory heartbeat monitoring method. It uses a new vital sign data extraction method to accurately discriminate vital sign data by the amplitude contribution of each frequency band. Experiments show that RMVS has a respiration monitoring error of less than 1.52 Beat Per Minute (BPM) within 3m.
利用毫米波雷达远程监测生命体征
室内生命体征监测有利于人们的健康生活。然而,监测距离限制了大多数现有的基于毫米波雷达的生命体征监测方法。对回波信号进行增强和分析有助于提高监测距离。本文提出采用毫米波雷达(RMVS)方法进行生命体征远程监测,实现室内人员生命体征的远程监测。RMVS充分利用多天线来表征来自胸腔的反射信号。通过信号叠加,增强了动态生命体征信号,抑制了环境静态杂波。RMVS通过叠加不同距离的多普勒信息,构建距离与微多普勒的映射关系。解决了传统雷达呼吸心跳监测方法在视距(LOS)和非视距(NLOS)路径下胸廓定位精度低的问题。该算法采用了一种新的生命体征数据提取方法,通过各频段的幅度贡献来准确区分生命体征数据。实验表明,RMVS在3米内的呼吸监测误差小于1.52 BPM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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