基于FMCW雷达的非接触式医疗健康监测技术综述

方震; 简璞; 张浩; 姚奕成; 耿芳琳; 刘畅宇; 闫百驹; 王鹏; 杜利东; 陈贤祥

doi:10.12000/JR22019

基于FMCW雷达的非接触式医疗健康监测技术综述

DOI: 10.12000/JR22019

方震^{1, 2, 4, ,},
简璞^{1, 2},
张浩^{1, 2},
姚奕成^{1, 2},
耿芳琳^{1, 2},
刘畅宇^{1, 2},
闫百驹^{1, 3},
王鹏¹,
杜利东¹,
陈贤祥¹

1.
中国科学院空天信息创新研究院北京 100049
2.
中国科学院大学电子电气与通信工程学院北京 100094
3.
上海交通大学电子信息与电气工程学院上海 200240
4.
中国医学科学院个性化呼吸慢病管理创新单元北京 100049

基金项目: 国家重点研发计划(2020YFC1512304, 2020YFC2003703)，中国医学科学院医学与健康科技创新工程项目(2019-I2M-5-019)

详细信息

作者简介:
方　震(1976–)，男，安徽巢湖人，中国科学院空天信息创新研究院研究员，博士生导师。研究方向为新型医疗电子检测与医学人工智能

简　璞(1997–)，男，安徽合肥人，中国科学院空天信息创新研究院在读硕士研究生。主要研究方向为智能医疗健康监测技术

张　浩(1997–)，男，山东济南人，中国科学院空天信息创新研究院在读博士研究生。主要研究方向为智能医疗健康监测技术和医疗物联网

通讯作者:
方震 zfang@mail.ie.ac.cn

责任主编：吴一戎 Corresponding Editor: WU Yirong
中图分类号: TN95; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-19
- 修回日期: 2022-03-04
- 网络出版日期: 2022-03-31
- 刊出日期: 2022-06-28

Review of Noncontact Medical and Health Monitoring Technologies Based on FMCW Radar

FANG Zhen^{1, 2, 4
, ,},
JIAN Pu^{1, 2},
ZHANG Hao^{1, 2},
YAO Yicheng^{1, 2},
GENG Fanglin^{1, 2},
LIU Changyu^{1, 2},
YAN Baiju^{1, 3},
WANG Peng¹,
DU Lidong¹,
CHEN Xianxiang¹

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100049, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China
3.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
4.
Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing 100049, China

Funds: The National Key Research and Development Project (2020YFC1512304, 2020YFC2003703), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-019)

More Information

Corresponding author: FANG Zhen, zfang@mail.ie.ac.cn

摘要

摘要: 非接触式的医疗健康监测系统解决了用户依从性问题，避免了佩戴电极、传感设备进行监测带来的不舒适感，更有助于将健康监测融入日常生活。非接触式监测手段具有持续地监测用户健康状况的潜力，能够在突发急性医疗事件出现时及时示警，且能够满足新生儿、烧伤患者、传染病患者等特殊人群的监测需求。调频连续波(FMCW)雷达能够同时捕获雷达视场内目标的距离、速度信息，可用于非接触式地监测用户的心率、呼吸率等生理体征及跌倒等行为动作，且从技术上易于单片集成，成本可控，因此在医疗健康监测领域有着重要的应用价值。该文首先阐述了将FMCW雷达应用于非接触式医疗健康监测技术的理论基础，然后系统性地归纳了该领域中的典型前沿应用，最后总结了基于FMCW雷达的医疗健康应用这一领域的研究现状及局限性，并对其应用前景与潜在的研究方向进行了展望。
- FMCW雷达 /
- 非接触式监测 /
- 生理体征监测 /
- 睡眠监测 /
- 跌倒检测
Abstract: A contactless health monitoring system can contribute to health assessment in daily life by reducing appliance usage and avoiding discomfort from wearing electrodes or sensors. Such contactless approaches have the potential to continuously monitor the health status of users, alert patients and health personnel in time when acute medical emergencies occur, and meet the monitoring demands of special populations, such as newborns, burn patients, and patients with infectious diseases. The Frequency-Modulated Continuous-Wave (FMCW) radar can measure the range and velocity of sensing targets and be widely applied in heart and respiration rate monitoring and fall detection. Moreover, advances in FMCW radar have enabled low-cost radar-on-chip and antenna-on-chip systems. Thus, FMCW radar has vital application value in the medical and health monitoring fields. In this study, first, we introduce the basic knowledge of the application of FMCW radar in contactless health monitoring. Then, we systematically review the advanced applications and latest papers in this field. Finally, we summarize the present situations and limitations and provide a brief outlook for the application prospects and potential future research in the field.
- FMCW radar /
- Contactless monitoring /
- Vital sign /
- Sleep monitoring /
- Falling detection

HTML全文

图 1 基于FMCW雷达的接收信号提取微多普勒信号与计算Range-Doppler图的信号处理流程。其中多个Chirp的IF信号经过FFT变换后得到的二维矩阵称为Range Profile

Figure 1. The signal processing flow of extracting micro-Doppler signal and calculating Range-Doppler map based on the received signal of FMCW radar. The two-dimensional matrix of multiple Chirp IF signals after FFT transform is called Range Profile

下载: 全尺寸图片幻灯片

图 2 SFCW雷达信号时频图

Figure 2. Time-frequency graph of SFCW radar

下载: 全尺寸图片幻灯片

图 3 FMCW雷达天线阵列计算Range-Angle图的原理示意图

Figure 3. The schematic diagram of Range-Angle diagram based on FMCW radar antenna array

下载: 全尺寸图片幻灯片

表 1 基于FMCW雷达的心率、呼吸率监测研究现状总结

Table 1. Summary of heart rate and respiratory rate monitoring based on FMCW radar

作者	信号获取方法	生理参数估计方法	实验设置	监测指标
Adib等人^[21]	微多普勒信号提取	频谱分析	被试者保持静止，与雷达相距1 m	RR准确率中位数为99.3%，HR准确率中位数为98.5%
Mercuri等人^[56]	微多普勒信号提取	频谱分析	2名被试者，静止，与雷达距离分别为2.6 m, 5.4 m	98.5%的RR估计误差小于3 次/ min, 95.5%的HR估计误差小于3 次/min
Wang等人^[22]	Beamforming，微多普勒信号提取	频谱分析	3名被试者，2名被试者与雷达距离1 m，AOA相差60°，1名被试者与雷达距离为1.5 m，保持静止	RR, HR平均准确率大于92.8%
Chen等人^[58]	相控阵技术，微多普勒信号提取	频谱分析	2名被试者，与雷达距离相同，约2 m，被试者间距离1 m，静止	97.8%的RR估计误差小于1.5 次/min, 93.6%的HR估计误差小于3 次/min
Sun等人^[59]	EMD，微多普勒信号提取	频谱分析	被试者与雷达间距1.0～2.5 m，静止	HR估计误差RMSE为2.03～5.83 次/min
Wang等人^[45]	VMD，微多普勒信号提取	峰值检测	被试者与雷达间距0.5～2.0 m，AOA为0～60°，静止	IBI RMSE为29.850～68.974 ms
Toda等人^[60]	CNN，微多普勒信号提取	QRS波群检测	被试者静止，距离2.5 m	IBI MAE为17.8 ms
Ha等人^[61]	Beamforming，CNN，微多普勒信号提取	Unet^[62]	被试者静止，面向雷达，距离25～50 cm	心脏收缩期、舒张期等心脏活动检测准确率90%，召回率为69.8%
Zheng等人^[63]	CFAR，多变量VMD，微多普勒信号提取	频谱分析，峰值检测	被试者驾驶汽车，在不同路况下行驶	RR 误差中位数为 0.06 次/min, HR MAE误差中位数为 0.6 次/ min, IBI误差中位数约50 ms
Chen等人^[64]	深度对比学习算法，微多普勒信号提取	频谱分析，峰值检测	被试者存在步行，坐下/站起等大幅度肢体运动	RR HR的MAPE为2% ，3%；不同肢体运动下IBI误差中位数为20～40 ms

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表 2 基于FMCW雷达的跌倒检测研究现状总结

Table 2. Summary of research status of falling detection based on FMCW radar

作者	雷达特征信息	算法概述	是否在新用户/ 新环境下测试	是否需要采集跌倒样本	非跌倒/跌倒样本比例	检测指标
Jokanovic等人^[125]	Range Profile，Range-Doppler图	Autoencoder+ Logistic回归	否	是	43:17	Acc: 96%
Tian等人^[128]	Range-Angle图	级联CNN分类器	是	是	450000:293	F1: 0.929
元志安等人^[126]	Range-Doppler图	CNN+LSTM	是	是	1:1	Acc: 96.67%
Wang等人^[127]	IF信号	LKCNN	否	是	1:1	Acc: 95.24%
Jin等人^[46]	点云	VAE+RNN	否	否	4:1	Acc: 98%
Li等人^[40]	Range-Doppler图	LSTM	是	是	5:1	Acc: 96%

下载: 导出CSV

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