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 CSTR: 32380.14.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
计量
- 文章访问数:
- HTML全文浏览量:
- PDF下载量:
- 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

1. 引言

基于导航卫星的双基地SAR(Bistatic Synthetic Aperture Radar based on Global Navigation Satellite System, GNSS-BSAR)是空-地双基地SAR中一种典型的应用^[1]，使用在轨的导航卫星作为发射源，地面部署接收机(地基、车载、机载)构成双基地SAR系统^[2]。由于导航星座的日趋完善，其全球覆盖性以及重轨特性所带来的优势是其他照射源暂时所不能替代的，其中以地基接收机为主的导航卫星干涉合成孔径雷达(Interference Synthetic Aperture Radar based on the Global Navigation Satellite System, GNSS-InSAR)在场景形变监测领域有着广阔的应用前景^[3]，成为了近年来研究热点。

在GNSS-BSAR系统成像方面，已有研究者分别使用不同的导航卫星星座进行了成像验证，包括了北斗^[4,5]、GPS^[6]、格洛纳斯^[7]、伽利略^[8]。除此以外文献[9]还提出了多角度融合方法以增强图像信噪比。在形变监测方面，来自伯明翰大学的学者们^[10]使用直达波天线，配合长约50 m的线缆构建了理想点目标，并使用格洛纳斯作为发射源，首次实现了精度约为1 cm的1维形变反演结果。该实验初步验证了GNSS-InSAR应用于形变监测的可行性。为了进一步验证场景形变监测的可能，2017年文献[11]通过对接收机进行高精度移位来模拟场景建筑形变，成功反演出了形变，精度约为1 cm。在3维形变方面，2018年北京理工大学的技术团队^[12]通过人为构建转发器，进行了精度可控的强点目标形变模拟，使用我国的北斗IGSO卫星，成功实现了精度优于5 mm的3维形变反演，这些验证性实验充分表明了GNSS-InSAR应用于场景形变检测的可能。

若要实现GNSS-InSAR场景的3维形变反演，需要同时至少3颗卫星从不同角度照射场景。由于GNSS-InSAR系统的拓扑高度非对称性以及导航信号的窄带特性^[13]，加上导航卫星的重轨并非是严格意义上的重轨，除了不可避免的空间基线外，重轨时间也并非严格一致，因此在实际数据采集中，需要对系统构型以及数据采集时间进行严格的优化设计。文献[14]提出了一种联合优化方法，解决了面向大场景下的多星多角度构型优化问题，配合多个接收机实现综合分辨性能优异的大场景成像。文献[15]提出了空间去相干的理论描述框架，表明了空间去相干在GNSS-InSAR中的必要性，但未对数据采集时间进行说明。从当前实际情况出发，不精确的数据采集时间可能会造成存储资源浪费，空间去相干导致的数据截取进一步降低了数据有效性。具体如图1所示：

图 1 数据截取与有效数据示意图

Figure 1. Effective data interception diagram

下载: 全尺寸图片幻灯片

针对上述问题，本文提出了一种GNSS-InSAR场景连续数据采集优化方法，通过结合当前数据的卫星轨迹和两行星历数据文件(STK Two-Line Element sets, TLE)预测轨迹，基于相干系数轨迹对齐，获取卫星重轨时间间隔，得到最优的数据采集策略，从源头上降低数据的空间去相干性，提升所采集数据有效性，节约存储资源。在第2部分对GNSS-InSAR场景数据采集优化方法进行了详细介绍。第3部分针对提出的方法进行了实验设计，开展了实测数据采集，并针对采集的数据进行了初步分析。第4部分对全文进行总结。

2. GNSS-InSAR场景数据采集优化模型

2.1 GNSS-InSAR相干系数理论模型

对于GNSS-InSAR图像而言，经过保相成像处理后，场景中任意一点 $\left( {{x_0},{y_0}} \right)$ 的像素信息分别对应分辨单元内所有散射体回波的相参叠加，可建模为

$\begin{aligned} s\left( {{x_0},{y_0}} \right) =& \iint f\left( {x,y;t} \right){\rm{exp}}\left[ {{\rm{ - j}}\frac{{2{\rm{\pi}} }}{\lambda }r\left( {x,y;{ P}} \right)} \right]\\ & \cdot {W}\left( {{x - }{{x}_0}{,y - }{{y}_0};{ P}} \right){\rm{d}}{x}{\rm{d}}{y} +{n}\left( {{{x}_0},{{y}_0};{t}} \right)\\ \end{aligned}$

(1)

其中， ${f}\left( {{x},{y};{t}} \right)$ 为时间 $t$ 下的地表散射系数， ${{P}}$ 为对应的合成孔径中心位置矢量， ${W}\left( {{x},{y};{ P}} \right)$ 表示系统的点扩散函数(PSF), ${n}\left( {{x},{y};{t}} \right)$ 为图像的加性噪声。对于SAR图像的同名点像素，其相干系数可表示为^[16]

$\rho =\frac{{\displaystyle\iint {{{s}_{\rm{m}}}\left( {{{x}_0},{{y}_0}} \right){s}_{\rm{s}}^{\rm{*}}\left( {{{x}_0},{{y}_0}} \right){\rm{d}}{x}{\rm{d}}{y}}}}{{\sqrt {\displaystyle\iint {{{s}_{\rm{m}}}\left( {{{x}_0},{{y}_0}} \right){s}_{\rm{m}}^{\rm{*}}\left( {{{x}_0},{{y}_0}} \right){\rm{d}}{x}{\rm{d}}{y}}\displaystyle\iint {{{s}_{\rm{s}}}\left( {{{x}_0},{{y}_0}} \right){s}_{\rm{s}}^{\rm{*}}\left( {{{x}_0},{{y}_0}} \right){\rm{d}}{x}{\rm{d}}{y}}} }}$

(2)

其中，下标m表示主图像， ${\rm{s}}$ 表示辅图像。根据柯西不等式可以判断： $0 \le \rho \le 1$ ，当 $\rho = 0$ 时表示同名点完全不相干，当 $\rho = 1$ 时，同名点完全相干。

将点目标像素模型式(1)带入到式(2)并化简得到

$\rho =\frac{\displaystyle\iint{{{s}_{{\rm av}}}\left( x,y \right){\rm exp}\left[ -{\rm j}\frac{2{\rm{\pi}} }{\lambda }\bigr( r\left( x,y;{{{P}}_{{\rm m}}} \bigr)-r\left( x,y;{{{P}}_{{\rm s}}} \right) \right) \right]}{{\Bigr| W\left( x-{{x}_{0}},y-{{y}_{0}};{{{P}}_{{\rm m}}} \right) \Bigr|}^{2}}{\rm d}x{\rm d}y}{\sqrt{\begin{array}{l} \left( \displaystyle\iint{{{s}_{{\rm m}}}\left( x,y \right){\rm exp}\left[ -{\rm j}\frac{2{\rm{\pi}} }{\lambda }r\left( x,y;{{{{P}}}_{{\rm m}}} \right) \right]}{{\Bigr| W\left( x-{{x}_{0}},y-{{y}_{0}};{{{P}}_{{\rm m}}} \right) \Bigr|}^{2}}{\rm d}x{\rm d}y+{{n}_{{\rm m}}} \right) \\ \times\left( \displaystyle\iint{{{s}_{{\rm s}}}\left( x,y \right){\rm exp}\left[ {\rm +j}\frac{2{\rm{\pi}} }{\lambda }r\left( x,y;{{{P}}_{{\rm s}}} \right) \right]}{{\Bigr| W\left( x-{{x}_{0}},y-{{y}_{0}};{{{P}}_{{\rm m}}} \right) \Bigr|}^{2}}{\rm d}x{\rm d}y+{{n}_{{\rm s}}} \right) \end{array}}}$

(3)

式(3)的推导使用了如下近似：

(1) 由于导航卫星的高轨道特性，使得 ${W}\left( {x - }{{x}_0},\right.$ $\left.{{y - }{{y}_0};{{ P}_{\rm{m}}}} \right) \approx {W}\left( {{x - }{{x}_0},{y - }{{y}_0};{{ P}_{\rm{s}}}} \right)$ 成立；

(2) 相邻两天的噪声相干系数为0，即

${n}\left( {{{x}_0},{{y}_0};{{t}_{\rm{m}}}} \right) \times {n}\left( {{{x}_0},{{y}_0};{{t}_{\rm{s}}}} \right) = 0$

(4)

(3) 相邻两天的目标散射系数为 ${{s}_{{\rm{av}}}}\left( {{x},{y}} \right)$ ，即

${{s}_{{\rm{av}}}}\left( {{x},{y}} \right) \approx {f}\left( {{x},{y};{{t}_{\rm{m}}}} \right){{f}^{\rm{*}}}\left( {{x},{y};{{t}_{\rm{s}}}} \right)$

(5)

对式(3)中的相干系数 $\rho$ 进一步分解得到

$\rho = {\rho _{{\rm{th}}}} \times {\rho _{{\rm{ti}}}} \times {\rho _{{\rm{sp}}}}$

(6)

其中，热噪声相干系数 ${\rho _{{\rm{th}}}}$ 与时间相干系数 ${\rho _{{\rm{ti}}}}$ 分别由系统与实际目标决定。

对于PS点^[17]而言，地表散射系数相对稳定，不随时间变化，同时为了便于后续分析，假定散射系数为1得到空间相干系数 ${\rho _{{\rm{sp}}}}$ 的简化式为

${\rho _{{\rm{sp}}}} = \frac{{\displaystyle\iint {{\rm{exp}}\left[ {{ - }{\rm{j}}\dfrac{{2{\rm{\pi}} }}{\lambda }\bigr( {{r}\left( {{x},{y};{{ P}_{\rm{m}}}} \right){ - r}\left( {{x},{y};{{ P}_{\rm{s}}}} \right)} \bigr)} \right]{{\Bigr| {{W(x - }{{x}_0},{y - }{{y}_0};{{ P}_{\rm{m}}}{)}} \Bigr|}^2}{\rm{d}}{x}{\rm{d}}{y}}}}{{\sqrt {\left( {\displaystyle\iint {{{\Bigr| {{W}\left( {{x - }{{x}_0},{y - }{{y}_0};{{ P}_{\rm{m}}}} \right)} \Bigr|}^2}{\rm{d}}{x}{\rm{d}}{y}}} \right)\left( {\displaystyle\iint {{{\Bigr| {{W(x - }{{x}_0},{y - }{{y}_0};{{ P}_{\rm{m}}}{)}} \Bigr|}^2}{\rm{d}}{x}{\rm{d}}{y}}} \right)} }}$

(7)

从式(7)推导结果可以知道，空间基线主要是影响 ${r}\left( {{x},{y};{ P}} \right)$ 从而导致空间去相干。

2.2 数据采集优化模型

导航卫星的重轨时间并非稳定不变，因此需要对数据采集时间进行有效预测，从源头上降低空间去相干，提高数据有效性。

假定主图像数据采集时间为 ${{t}_{\rm{m}}}$ ，该采集时间可以通过文献[14]中的广义优化模型进行求解，辅图像数据采集时间为 ${{t}_{\rm{s}}}={{t}_{\rm{m}}}+\Delta {t}$ , $\Delta t$ 为时间间隔，那么最优化数据采集模型可通过式(7)推导而来

$\begin{split} \Delta {t }=&{\rm{arg}}\;{\rm{max}}\left\{ \iint {{\left| {{\widetilde W}\left( {{x},{y};{{ P}_{\rm{m}}}} \right)} \right|}^2} {\rm{exp}}\left[ {{ - }{\rm{j}}\frac{{2{\rm{\pi}} }}{\lambda }\bigr( {{r}\left( {{x},{y};{{ P}_{\rm{m}}}} \right){ - r}\left( {{x},{y};{ P}\left( {{{t}_{\rm{m}}}+\Delta {t}} \right)} \right)} \bigr)} \right] {\rm{d}}{x}{\rm{d}}{y} \right\} \end{split}$

(8)

其中， ${\widetilde W}\left( {{x},{y};{{ P}_{\rm{m}}}} \right)$ 为 ${t_{\rm{m}}}$ 下等效归一化PSF, ${ P}\left( {{{t}_{\rm{m}}} +}\right.$ $\left.\Delta {t} \right)$ 为 $\Delta {t}$ 时间偏置下得合成孔径中心位置矢量。

第1天数据采集需要进行实验设计以确定最优数据采集时刻，往后的重轨天数据采集可以根据数据采集优化模型，同时结合星历文件进行预测。整体的预测流程如图2所示， ${n}$ 为任意一天采集的数据， ${k}$ 为重轨天数间隔。

图 2 GNSS-InSAR数据采集时间优化流程

Figure 2. Time optimization process of GNSS-InSAR data acquisition

下载: 全尺寸图片幻灯片

实际卫星位置对应的实际时间设为 ${{t}_{n}}$ ，经过模型优化得到的时间偏差为 $\Delta {t}$ ，那么第 ${n} + {k}$ 天对应的实际数据采集时间可表示为

${{t}_{{n}+{k}}} = {{t}_{n}} + \Delta {t}$

(9)

3. 实验验证

3.1 GNSS-InSAR场景3维形变反演实验设计

对于固定场景的形变监测，首次数据采集的时候需要严格设计系统构型，使分辨率达到最优化。本次实验接收机部署在北京理工大学信息科学试验楼楼顶西北角，实施监测场景位于西偏北30°。使用理论分辨率计算公式^[18]对该场景进行分辨率设计。仿真参数具体参见表1。

表 1 数据采集试验仿真参数

Table 1. Data acquisition test simulation parameters

参数	值
照射源	北斗 IGSO1～5
PRF	1000 Hz
带宽	10.23 MHz
合成孔径时间	600 s
TLE文件更新日期	2019年4月29日
预定数据采集日期	2019年4月30日

下载: 导出CSV

| 显示表格

以分辨单元面积作为判定依据，得到预定采集日期当天全时段下各个卫星在预定场景下所能得到的分辨单元面积如图3所示。

图 3 全时段下场景分辨单元面积

Figure 3. Scene resolution unit area in full time

下载: 全尺寸图片幻灯片

为了实现3维形变反演，需要同一时间下有3颗卫星对场景进行照射。图3中10点前后与17点前后满足当前场景上空有3颗IGSO卫星可见的条件。更进一步，为了使分辨单元面积达到最优，可以得到具体的数据采集时间。具体如图4红框标注，分别是9点30分前后与17点30分前后。

图 4 首次数据采集时间设计结果

Figure 4. Design results of first data acquisition time

下载: 全尺寸图片幻灯片

为了配合实验，在场景布置转发器，整体的系统构型如图5所示。

图 5 GNSS-InSAR场景3维形变反演实验拓扑构型设计结果

Figure 5. GNSS-InSAR topological configuration design results of 3D deformation retrieval experiment

下载: 全尺寸图片幻灯片

3.2 重轨数据采集时间优化

以2019年4月30日采集的实测数据作为第 ${n}$ 天数据，对于北斗的IGSO而言，重轨时间约为1天，即 ${m}$ =1，同时下载当天最新的TLE文件。以IGSO1为例，结合图2进行详细说明：

(1) 使用实测数据的直达波进行卫星位置解算，同时根据TLE文件推算当天和相邻天的卫星轨迹。经过相干系数轨迹匹配之后，得到的轨迹如图6所示。

图 6 对齐后的TLE卫星轨迹与实测数据卫星轨迹

Figure 6. Aligned TLE satellite trajectory and measured data satellite trajectory

下载: 全尺寸图片幻灯片

(2) 以匹配得到的TLE卫星轨迹作为参考，对重轨天的TLE卫星轨迹进行数据采集优化模型求解，系统的PSF与优化模型仿真结果分别如图7与图8所示。

图 7 场景[–147, 20, 0]处理论PSF

Figure 7. Theoretical PSF in scene at position of [–147, 20, 0]

下载: 全尺寸图片幻灯片

图 8 数据采集优化模型仿真结果

Figure 8. Simulation results of data acquisition optimization model

下载: 全尺寸图片幻灯片

对图8的结果分析可知，第1个峰值点为其本身，由于空间基线为0，相干系数为1。第2个峰值点相干系数为0.999644，满足除了第1个峰值点外相邻天相干系数最大值条件，因此第2个峰值点就是最佳重轨时的空间相干系数。此时经过模型优化得到的时间间隔为： $\Delta t = 86163\;{\rm{s}} = 23\;{\rm{h}}\;56\;\min 3\;{\rm{s}}$ ，结合第1天的实测数据轨迹对应的时间 ${t_1} = 9\;{\rm{h}}\;26\;\min \;0\;{\rm{s}}$ ，第2天准确的数据采集时间为： ${t_2} = 9\;{\rm{h}}\;22\;\min 3\;{\rm{s}}$ 。

3.3 结果分析

为了说明优化结果的正确性，在实验场景中放置转发器模拟理想点目标(图5)，同时按照优化后的时间进行5月1日数据采集。实际采集时间为 $9\;{\rm{h}}\;21\;\min \;53\;{\rm{s}}$ ，总采集时间约650 s。相邻两天的空间相干系数轨迹匹配结果如图9所示。

图 9 实测数据重轨空间相干系数

Figure 9. The spatial coherence coefficient of measured data

下载: 全尺寸图片幻灯片

从图9中峰值点位置来看，重轨数据采集优化模型得到的结果和实际结果相吻合。为了进一步说明，图10给出了IGSO1卫星实测数据成像结果。

图 10 场景成像结果

Figure 10. Imaging results of scene

下载: 全尺寸图片幻灯片

对相邻两天的图像相干系数进行求解，得到图11所示结果。在同一坐标系下，仿真目标位于[–147, 20, 0]，空间相干系数为0.999644；转发器位于[–147, 20, 0]，相干系数为0.9996；两者的相干系数基本保持一致。

图 11 相干系数结果

Figure 11. Coherence coefficient result

下载: 全尺寸图片幻灯片

图9与图10的结果表明经过数据采集优化模型后得到的时间间隔与实际卫星轨迹的重轨时间相互吻合，在保证600 s预期合成孔径时间下，可以最大限度减少数据采集时间，节约存储资源。同时避免后期由于数据对齐带来的数据有效性降低问题。

4. 结论

在GNSS-InSAR场景1维/3维形变反演应用中，针对由于导航卫星重轨时间的非严格一致性与有效数据截取带来的数据冗余，数据有效性低等问题，本文提出了一种面向GNSS-InSAR场景数据采集的优化模型，采用实测数据与TLE文件相结合，根据当天数据采集时间，预测相邻天重轨时间，从而实现精确的数据采集。实测数据验证结果表明了数据采集时间优化模型的正确性。该方法的提出有利于GNSS-InSAR场景1维/3维形变反演实验的开展，在降低原始数据冗余度基础上，保证了有效数据时间长度大于预期合成孔径时间。

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

下载: 导出CSV

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

参考文献(137)

[1]	LU Shan, WANG Anzhi, JING Shenqi, et al. A study on service-oriented smart medical systems combined with key algorithms in the IoT environment[J]. China Communications, 2019, 16(9): 235–249. doi: 10.23919/JCC.2019.09.018
[2]	AL-MAHMUD O, KHAN K, ROY R, et al. Internet of things (IoT) based smart health care medical box for elderly people[C]. 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020: 1–6.
[3]	VILLENEUVE E, HARWIN W, HOLDERBAUM W, et al. Reconstruction of angular kinematics from wrist-worn inertial sensor data for smart home healthcare[J]. IEEE Access, 2017, 5: 2351–2363. doi: 10.1109/ACCESS.2016.2640559
[4]	MAHFOUZ M R, KUHN M J, and TO G. Wireless medical devices: A review of current research and commercial systems[C]. 2013 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems, Austin, USA, 2013: 16–18.
[5]	PARK E, KIM J H, NAM H S, et al. Requirement analysis and implementation of smart emergency medical services[J]. IEEE Access, 2018, 6: 42022–42029. doi: 10.1109/ACCESS.2018.2861711
[6]	LIU Jian, CHEN Yingying, WANG Yan, et al. Monitoring vital signs and postures during sleep using WiFi signals[J]. IEEE Internet of Things Journal, 2018, 5(3): 2071–2084. doi: 10.1109/JIOT.2018.2822818
[7]	WANG Hao, ZHANG Daqing, WANG Yasha, et al. RT-Fall: A real-time and contactless fall detection system with commodity WiFi devices[J]. IEEE Transactions on Mobile Computing, 2017, 16(2): 511–526. doi: 10.1109/TMC.2016.2557795
[8]	WANG Xuyu, YANG Chao, and MAO Shiwen. PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices[C]. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, USA, 2017: 1230–1239.
[9]	LIN Feng, SONG Chen, ZHUANG Yan, et al. Cardiac scan: A non-contact and continuous heart-based user authentication system[C]. The 23rd Annual International Conference on Mobile Computing and Networking, Snowbird, USA, 2017: 315–328.
[10]	WILL C, SHI K, SCHELLENBERGER S, et al. Local pulse wave detection using continuous wave radar systems[J]. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2017, 1(2): 81–89. doi: 10.1109/JERM.2017.2766567
[11]	RAHMAN T, ADAMS A T, RAVICHANDRAN R V, et al. DoppleSleep: A contactless unobtrusive sleep sensing system using short-range doppler radar[C]. The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 2015: 39–50.
[12]	YANG Yang, HOU Chunping, LANG Yue, et al. Open-set human activity recognition based on micro-Doppler signatures[J]. Pattern Recognition, 2019, 85: 60–69. doi: 10.1016/j.patcog.2018.07.030
[13]	LARSON E C, GOEL M, BORIELLO G, et al. SpiroSmart: Using a microphone to measure lung function on a mobile phone[C]. The 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, USA, 2012: 280–289.
[14]	ZHANG Fusang, WANG Zhi, JIN Beihong, et al. Your smart speaker can “hear” your heartbeat![J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(4): 161. doi: 10.1145/3432237
[15]	陆佳鑫. 基于深度神经网络的人体跌倒碰撞前行为检测研究[D]. [硕士论文], 电子科技大学, 2021. LU Jiaxin. A research of human pre-impact fall detection based on deep neural network[D]. [Master dissertation], University of Electronic Science and Technology of China, 2021.
[16]	SADREAZAMI H, BOLIC M, and RAJAN S. Fall detection using standoff radar-based sensing and deep convolutional neural network[J]. IEEE Transactions on Circuits and Systems II:Express Briefs, 2020, 67(1): 197–201. doi: 10.1109/TCSII.2019.2904498
[17]	PENG Zhengyu and LI Changzhi. Portable microwave radar systems for short-range localization and life tracking: A review[J]. Sensors, 2019, 19(5): 1136. doi: 10.3390/s19051136
[18]	JARDAK S, ALOUINI M S, KIURU T, et al. Compact mmWave FMCW radar: Implementation and performance analysis[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34(2): 36–44. doi: 10.1109/MAES.2019.180130
[19]	PATOLE S M, TORLAK M, WANG Dan, et al. Automotive radars: A review of signal processing techniques[J]. IEEE Signal Processing Magazine, 2017, 34(2): 22–35. doi: 10.1109/MSP.2016.2628914
[20]	LI Changzhi, PENG Zhengyu, HUANG T Y, et al. A review on recent progress of portable short-range noncontact microwave radar systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2017, 65(5): 1692–1706. doi: 10.1109/TMTT.2017.2650911
[21]	ADIB F, MAO Hongzi, KABELAC Z, et al. Smart homes that monitor breathing and heart rate[C]. The 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea, 2015: 837–846.
[22]	WANG Fengyu, ZHANG Feng, WU Chenshu, et al. ViMo: Multiperson vital sign monitoring using commodity millimeter-wave radio[J]. IEEE Internet of Things Journal, 2021, 8(3): 1294–1307. doi: 10.1109/JIOT.2020.3004046
[23]	CHEN Baozhan, QIAO Siyuan, ZHAO Jie, et al. A security awareness and protection system for 5G smart healthcare based on zero-trust architecture[J]. IEEE Internet of Things Journal, 2021, 8(13): 10248–10263. doi: 10.1109/JIOT.2020.3041042
[24]	李健. 24GHz调频连续波雷达信号处理技术研究[D]. [硕士论文], 南京理工大学, 2017. LI Jian. Research on signal processing technology of 24GHz FMCW radar[D]. [Master dissertation], Nanjing University of Science and Technology, 2017.
[25]	李艳莉. 毫米波通信技术的研究现状和进展[C]. 四川省通信学会2010年学术年会论文集, 成都, 2010: 46–49. LI Yanli. Research status and progress of millimeter wave communication technology[C]. Papers of the 2010 Annual Conference of Sichuan Communications Society, Chengdu, China, 2010: 46–49.
[26]	CHADWICK P E. Regulations and standards for wireless applications in eHealth[C]. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007: 6170–6173.
[27]	YANG Xiaodong, FAN Dou, REN Aifeng, et al. Sleep apnea syndrome sensing at C-band[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2018, 6: 2701008. doi: 10.1109/JTEHM.2018.2879085
[28]	OHAYON M, WICKWIRE E M, HIRSHKOWITZ M, et al. National sleep foundation’s sleep quality recommendations: First report[J]. Sleep Health, 2017, 3(1): 6–19. doi: 10.1016/j.sleh.2016.11.006
[29]	张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7(5): 531–547. doi: 10.12000/JR18049 ZHANG Qun, HU Jian, LUO Ying, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7(5): 531–547. doi: 10.12000/JR18049
[30]	KEBE M, GADHAFI R, MOHAMMAD B, et al. Human vital signs detection methods and potential using radars: A review[J]. Sensors, 2020, 20(5): 1454. doi: 10.3390/s20051454
[31]	龙腾, 毛二可, 何佩琨. 调频步进雷达信号分析与处理[J]. 电子学报, 1998, 26(12): 84–88. doi: 10.3321/j.issn:0372-2112.1998.12.019 LONG Teng, MAO Erke, and HE Peikun. Analysis and processing of modulated frequency stepped radar signal[J]. Acta Electronica Sinica, 1998, 26(12): 84–88. doi: 10.3321/j.issn:0372-2112.1998.12.019
[32]	LV Hao, JIAO Teng, ZHANG Yang, et al. A novel method for breath detection via stepped-frequency continuous wave ultra-wideband (SFCW UWB) radars based on operational bandwidth segmentation[J]. Sensors, 2018, 18(11): 3873. doi: 10.3390/s18113873
[33]	张杨, 焦腾, 荆西京, 等. 生物雷达技术的研究现状与新进展[J]. 信息化研究, 2010, 36(10): 6–10, 13. doi: 10.3969/j.issn.1674-4888.2010.10.002 ZHANG Yang, JIAO Teng, JING Xijing, et al. Current state and progress of the technology of bioradar[J]. Informatization Research, 2010, 36(10): 6–10, 13. doi: 10.3969/j.issn.1674-4888.2010.10.002
[34]	黄文奎. 毫米波汽车防撞雷达的设计与实现[D]. [博士论文], 中国科学院研究生院, 2006. HUANG Wenkui. Design and production of millimeter-wave automotive radar for collision avoidance application[D]. [Ph. D. dissertation], University of Chinese Academy of Sciences, 2006.
[35]	赵锴. 汽车防碰撞系统雷达设计与信号处理[D]. [硕士论文], 青岛理工大学, 2018. ZHAO Kai. Design and signal processing on automotive anti-collision radar system[D]. [Master dissertation], Qingdao University of Technology, 2018.
[36]	胡程, 廖鑫, 向寅, 等. 一种生命探测雷达微多普勒测量灵敏度分析新方法[J]. 雷达学报, 2016, 5(5): 455–461. doi: 10.12000/JR16090 HU Cheng, LIAO Xin, XIANG Yin, et al. Novel analytic method for determining micro-Doppler measurement sensitivity in life-detection radar[J]. Journal of Radars, 2016, 5(5): 455–461. doi: 10.12000/JR16090
[37]	DING Chuanwei, HONG Hong, ZOU Yu, et al. Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6821–6831. doi: 10.1109/TGRS.2019.2908758
[38]	赵珍珍. 老年人跌倒检测算法的研究现状[J/OL]. 计算机工程与应用, http://kns.cnki.net/kcms/detail/11.2127.tp.20211112.0903.002.html. 2021. ZHAO Zhenzhen. Research status of elderly fall detection algorithms[J]. Computer Engineering and Applications, http://kns.cnki.net/kcms/detail/11.2127.tp.20211112.0903.002.html. 2021.
[39]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 770–778.
[40]	LI Haobo, SHRESTHA A, HEIDARI H, et al. Bi-LSTM network for multimodal continuous human activity recognition and fall detection[J]. IEEE Sensors Journal, 2020, 20(3): 1191–1201. doi: 10.1109/JSEN.2019.2946095
[41]	ALANAZI M A, ALHAZMI A K, YAKOPCIC C, et al. Machine learning models for human fall detection using millimeter wave sensor[C]. 2021 55th Annual Conference on Information Sciences and Systems (CISS), Baltimore, USA, 2021: 1–5.
[42]	BHATTACHARYA A and VAUGHAN R. Deep learning radar design for breathing and fall detection[J]. IEEE Sensors Journal, 2020, 20(9): 5072–5085. doi: 10.1109/JSEN.2020.2967100
[43]	韩文婷, 娄昊, 樊阳, 等. 一种改进的 MIMO 生物雷达人体目标检测跟踪联合自适应算法[J]. 信号处理, 2021, 37(11): 2227–2234. doi: 10.16798/j.issn.1003-0530.2021.11.025 HAN Wenting, LOU Hao, FAN Yang, et al. An improved joint adaptive algorithm for MIMO bio-radar human target detection and tracking[J]. Journal of Signal Processing, 2021, 37(11): 2227–2234. doi: 10.16798/j.issn.1003-0530.2021.11.025
[44]	ADIB F, KABELAC Z, KATABI D, et al. 3D tracking via body radio reflections[C]. The 11th USENIX Conference on Networked Systems Design and Implementation, Seattle, USA, 2014: 317–329.
[45]	WANG Fengyu, ZENG Xiaolu, WU Chenshu, et al. MmHRV: Contactless heart rate variability monitoring using millimeter-wave radio[J]. IEEE Internet of Things Journal, 2021, 8(22): 16623–16636. doi: 10.1109/JIOT.2021.3075167
[46]	JIN Feng, SENGUPTA A, and CAO Siyang. MmFall: Fall detection using 4-D mmWave radar and a hybrid variational RNN autoencoder[J]. IEEE Transactions on Automation Science and Engineering, 2020: 1–13. doi: 10.1109/TASE.2020.3042158
[47]	GUPTA S, RAI P K, KUMAR A, et al. Target classification by mmWave FMCW radars using machine learning on range-angle images[J]. IEEE Sensors Journal, 2021, 21(18): 19993–20001. doi: 10.1109/JSEN.2021.3092583
[48]	ZHANG Feng, WU Chenshu, WANG Beibei, et al. MmEye: Super-resolution millimeter wave imaging[J]. IEEE Internet of Things Journal, 2021, 8(8): 6995–7008. doi: 10.1109/JIOT.2020.3037836
[49]	HSU C Y, HRISTOV R, LEE G H, et al. Enabling identification and behavioral sensing in homes using radio reflections[C]. The 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 2019: 548.
[50]	OUAKNINE A, NEWSON A, REBUT J, et al. CARRADA dataset: Camera and automotive radar with range-angle-Doppler annotations[C]. 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021: 5068–5075.
[51]	陈功, 张业荣. 基于胶囊内窥镜的胃部肿瘤检测方法?[J]. 物理学报, 2016, 65(16): 194101. doi: 10.7498/aps.65.194101 CHEN Gong and ZHANG Yerong. A method of detecting stomach tumour based on capsule endoscopy[J]. Acta Physica Sinica, 2016, 65(16): 194101. doi: 10.7498/aps.65.194101
[52]	REIMER T, SACRISTAN J, and PISTORIUS S. Improving the diagnostic capability of microwave radar imaging systems using machine learning[C]. 2019 13th European Conference on Antennas and Propagation (EuCAP), Krakow, Poland, 2019: 1–5.
[53]	OMER M, MOJABI P, KURRANT D, et al. Proof-of-concept of the incorporation of ultrasound-derived structural information into microwave radar imaging[J]. IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2018, 3: 129–139. doi: 10.1109/JMMCT.2018.2865111
[54]	INAN O T, MIGEOTTE P F, PARK K S, et al. Ballistocardiography and seismocardiography: A review of recent advances[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(4): 1414–1427. doi: 10.1109/JBHI.2014.2361732
[55]	TAEBI A, SOLAR B E, BOMAR A J, et al. Recent advances in seismocardiography[J]. Vibration, 2019, 2(1): 64–86. doi: 10.3390/vibration2010005
[56]	MERCURI M, LORATO I R, LIU Yaohong, et al. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor[J]. Nature Electronics, 2019, 2(6): 252–262. doi: 10.1038/s41928-019-0258-6
[57]	MERCURI M, LU Yiting, POLITO S, et al. Enabling robust radar-based localization and vital signs monitoring in multipath propagation environments[J]. IEEE Transactions on Biomedical Engineering, 2021, 68(11): 3228–3240. doi: 10.1109/TBME.2021.3066876
[58]	CHEN Feng, JIANG Xiaonan, JEONG M G, et al. Multitarget vital signs measurement with chest motion imaging based on MIMO radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2021, 69(11): 4735–4747. doi: 10.1109/TMTT.2021.3076239
[59]	SUN Li, HUANG Shuaiming, LI Yusheng, et al. Remote measurement of human vital signs based on joint-range adaptive EEMD[J]. IEEE Access, 2020, 8: 68514–68524. doi: 10.1109/ACCESS.2020.2985286
[60]	TODA D, ANZAI R, ICHIGE K, et al. ECG signal reconstruction using FMCW radar and convolutional neural network[C]. 2021 20th International Symposium on Communications and Information Technologies (ISCIT), Tottori, Japan, 2021: 176–181.
[61]	HA U, ASSANA S, and ADIB F. Contactless seismocardiography via deep learning radars[C]. The 26th Annual International Conference on Mobile Computing and Networking, London, United Kingdom, 2020: 62.
[62]	RONNEBERGER O, FISCHER P, and BROX T. U-net: Convolutional networks for biomedical image segmentation[C]. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241.
[63]	ZHENG Tianyue, CHEN Zhe, CAI Chao, et al. V2iFi: In-vehicle vital sign monitoring via compact RF sensing[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 70. doi: 10.1145/3397321
[64]	CHEN Zhe, ZHENG Tianyue, CAI Chao, et al. MoVi-Fi: Motion-robust vital signs waveform recovery via deep interpreted RF sensing[C]. The 27th Annual International Conference on Mobile Computing and Networking, New Orleans, USA, 2021: 392–405.
[65]	AHMAD A, ROH J C, WANG Dan, et al. Vital signs monitoring of multiple people using a FMCW millimeter-wave sensor[C]. 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, USA, 2018: 1450–1455.
[66]	MERCURI M, SACCO G, HORNUNG R, et al. 2-D localization, angular separation and vital signs monitoring using a SISO FMCW radar for smart long-term health monitoring environments[J]. IEEE Internet of Things Journal, 2021, 8(14): 11065–11077. doi: 10.1109/JIOT.2021.3051580
[67]	胡锡坤, 金添. 基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法[J]. 雷达学报, 2016, 5(5): 462–469. doi: 10.12000/JR16103 HU Xikun and JIN Tian. Adaptive wavelet scale selection-based method for separating respiration and heartbeat in bio-radars[J]. Journal of Radars, 2016, 5(5): 462–469. doi: 10.12000/JR16103
[68]	HE Mi, NIAN Yongjian, and GONG Yushun. Novel signal processing method for vital sign monitoring using FMCW radar[J]. Biomedical Signal Processing and Control, 2017, 33: 335–345. doi: 10.1016/j.bspc.2016.12.008
[69]	DRAGOMIRETSKIY K and ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544. doi: 10.1109/TSP.2013.2288675
[70]	RIBEIRO A H, RIBEIRO M H, PAIXÃO G M M, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network[J]. Nature Communications, 2020, 11(1): 1760. doi: 10.1038/s41467-020-15432-4
[71]	ZHAO Mingmin, ADIB F, and KATABI D. Emotion recognition using wireless signals[C]. The 22nd Annual International Conference on Mobile Computing and Networking, New York, USA, 2016: 95–108.
[72]	UR REHMAN N and AFTAB H. Multivariate variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2019, 67(23): 6039–6052. doi: 10.1109/TSP.2019.2951223
[73]	CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, Vienna, Austria, 2020: 1597–1607.
[74]	祁富贵, 岳超, 梁福来, 等. SFCW生物雷达人体细粒度运动信号微多普勒特征增强方法研究[J]. 中国医疗设备, 2016, 31(2): 39–43, 94. doi: 10.3969/j.issn.1674-1633.2016.02.009 QI Fugui, YUE Chao, LIANG Fulai, et al. A study on the micro-Doppler signature enhanced technique for the finer-grained human activity signal acquired by the SFCW bio-radar[J]. China Medical Devices, 2016, 31(2): 39–43, 94. doi: 10.3969/j.issn.1674-1633.2016.02.009
[75]	张杨, 吕昊, 于霄, 等. 基于超宽谱雷达多目标穿墙探测定位技术的研究[J]. 医疗卫生装备, 2016, 37(8): 10–13. doi: 10.7687/J.ISSN1003-8868.2016.08.010 ZHANG Yang, LYU Hao, YU Xiao, et al. Research of through-wall detection and location technique for multihuman targets using ultra wideband radar[J]. Chinese Medical Equipment Journal, 2016, 37(8): 10–13. doi: 10.7687/J.ISSN1003-8868.2016.08.010
[76]	MA Yangyang, WANG Pengfei, XUE Huijun, et al. Non-contact vital states identification of trapped living bodies using ultra-wideband bio-radar[J]. IEEE Access, 2020, 9: 6550–6559. doi: 10.1109/ACCESS.2020.3048381
[77]	LIANG Fulai, LI Haonan, LIU Miao, et al. Autofocusing method for through-the-wall bioradar imagery of human vital signs[J]. The Journal of Engineering, 2019, 2019(21): 7597–7600. doi: 10.1049/joe.2019.0540
[78]	李廉林, 崔铁军. 智能电磁感知的若干进展[J]. 雷达学报, 2021, 10(2): 183–190. doi: 10.12000/JR21049 LI Lianlin and CUI Tiejun. Recent progress in intelligent electromagnetic sensing[J]. Journal of Radars, 2021, 10(2): 183–190. doi: 10.12000/JR21049
[79]	LI Lianlin, SHUANG Ya, MA Qian, et al. Intelligent metasurface imager and recognizer[J]. Light:Science & Applications, 2019, 8(1): 97. doi: 10.1038/s41377-019-0209-z
[80]	LIU Zhenyu, KONG Yongan, ZHANG Xin, et al. Vital sign extraction in the presence of radar mutual interference[J]. IEEE Signal Processing Letters, 2020, 27: 1745–1749. doi: 10.1109/LSP.2020.3026942
[81]	ZHANG Yang, QI Fugui, LV Hao, et al. Bioradar technology: Recent research and advancements[J]. IEEE Microwave Magazine, 2019, 20(8): 58–73. doi: 10.1109/MMM.2019.2915491
[82]	HUANG Xinming, SUN Ling, TIAN Tian, et al. Real-time non-contact infant respiratory monitoring using UWB radar[C]. 2015 IEEE 16th International Conference on Communication Technology (ICCT), Hangzhou, China, 2015: 493–496.
[83]	LI Chuantao, CHEN Fuming, JIN Jingxi, et al. A method for remotely sensing vital signs of human subjects outdoors[J]. Sensors, 2015, 15(7): 14830–14844. doi: 10.3390/s150714830
[84]	王健琪, 薛慧君, 吕昊, 等. 非接触生理信号检测技术[J]. 中国医疗设备, 2013, 28(11): 5–8, 80. doi: 10.3969/j.issn.1674-1633.2013.11.002 WANG Jianqi, XUE Huijun, LV Hao, et al. Non-contact detection technology for physiological signals[J]. Chinese Medical Devices, 2013, 28(11): 5–8, 80. doi: 10.3969/j.issn.1674-1633.2013.11.002
[85]	KUMAR S S, DASHTIPOUR K, ABBASI Q H, et al. A review on wearable and contactless sensing for COVID-19 with policy challenges[J]. Frontiers in Communications and Networks, 2021, 2: 636293. doi: 10.3389/frcmn.2021.636293
[86]	王健琪, 王海滨, 荆西京, 等. 呼吸、心率的雷达式非接触检测系统设计与研究[J]. 中国医疗器械杂志, 2001, 25(3): 132–135. WANG Jianqi, WANG Haibing, JING Xijing, et al. The study on non-contact detection of breathing and heartbeat based on radar principles[J] Chinese Journal of Medical Instrumentation, 2001, 25(3): 132–135.
[87]	SCHMIECH D, MULLER S, and DIEWALD A R. 4-channel I/Q-radar system for vital sign monitoring in a baby incubator[C]. 2018 19th International Radar Symposium (IRS), Bonn, Germany, 2018: 1–9.
[88]	SUN Guanghao, OKADA M, NAKAMURA R, et al. Twenty‐four‐hour continuous and remote monitoring of respiratory rate using a medical radar system for the early detection of pneumonia in symptomatic elderly bedridden hospitalized patients[J]. Clinical Case Reports, 2019, 7(1): 83–86. doi: 10.1002/ccr3.1922
[89]	ADHIKARI A, HETHERINGTON A, and SUR S. MmFlow: Facilitating at-home spirometry with 5G smart devices[C]. 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Rome, Italy, 2021: 1–9.
[90]	JORGENSEN G, DOWNEY C, GOLDIN J, et al. An australasian commentary on the AASM manual for the scoring of sleep and associated events[J]. Sleep and Biological Rhythms, 2020, 18(3): 163–185. doi: 10.1007/s41105-020-00259-9
[91]	BONNET M H and ARAND D L. Heart rate variability: Sleep stage, time of night, and arousal influences[J]. Electroencephalography and Clinical Neurophysiology, 1997, 102(5): 390–396. doi: 10.1016/S0921-884X(96)96070-1
[92]	彭小虎, 王国锋, 刘军, 等. 睡眠微观结构—CAP与睡眠质量评估[J]. 中国临床心理学杂志, 2013, 21(6): 920–923. doi: 10.16128/J.CNKI.1005-3611.2013.06.030 PENG Xiaohu, WANG Guofeng, LIU Jun, et al. Sleep microstructure—CAP and sleep quality assessment[J]. Chinese Journal of Clinical Psychology, 2013, 21(6): 920–923. doi: 10.16128/J.CNKI.1005-3611.2013.06.030
[93]	FONSECA P, DEN TEULING N, LONG Xi, et al. Cardiorespiratory sleep stage detection using conditional random fields[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 21(4): 956–966. doi: 10.1109/JBHI.2016.2550104
[94]	SCHULZ S, ADOCHIEI F C, EDU I R, et al. Cardiovascular and cardiorespiratory coupling analyses: A review[J]. Philosophical Transactions. Series A, Mathematical, Physical and Engineering Sciences, 2013, 371(1997): 20120191. doi: 10.1098/rsta.2012.0191
[95]	BARTSCH R P, LIU K K L, MA Q D Y, et al. Three independent forms of cardio-respiratory coupling: Transitions across sleep stages[C]. Computing in Cardiology 2014, Cambridge, USA, 2014: 781–784.
[96]	LONG Xi, FOUSSIER J, FONSECA P, et al. Analyzing respiratory effort amplitude for automated sleep stage classification[J]. Biomedical Signal Processing and Control, 2014, 14: 197–205. doi: 10.1016/j.bspc.2014.08.001
[97]	HSU C Y, AHUJA A, YUE Shichao, et al. Zero-Effort in-home sleep and insomnia monitoring using radio signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(3): 1–18. doi: 10.1145/3130924
[98]	ZHAO Mingmin, YUE Shichao, KATABI D, et al. Learning sleep stages from radio signals: A conditional adversarial architecture[C]. The 34th International Conference on Machine Learning, Sydney, Australia, 2017: 4100–4109.
[99]	TZENG E, HOFFMAN J, SAENKO K, et al. Adversarial discriminative domain adaptation[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 7167–7176.
[100]	GOTTLIEB D J and PUNJABI N M. Diagnosis and management of obstructive sleep apnea: A review[J]. JAMA, 2020, 323(14): 1389–1400. doi: 10.1001/jama.2020.3514
[101]	THOMAS R J, MIETUS J E, PENG C K, et al. Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method[J]. Sleep, 2007, 30(12): 1756–1769. doi: 10.1093/sleep/30.12.1756
[102]	BABOLI M, SINGH A, SOLL B, et al. Wireless sleep apnea detection using continuous wave quadrature Doppler radar[J]. IEEE Sensors Journal, 2020, 20(1): 538–545. doi: 10.1109/JSEN.2019.2941198
[103]	MENDONÇA F, MOSTAFA S S, RAVELO-GARCIA A G, et al. A review of obstructive sleep apnea detection approaches[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(2): 825–837. doi: 10.1109/JBHI.2018.2823265
[104]	NANDAKUMAR R, GOLLAKOTA S, and WATSON N. Contactless sleep apnea detection on smartphones[C]. The 13th Annual International Conference on Mobile Systems, Applications, and Services, Florence, Italy, 2015: 45–57.
[105]	ISLAM S M M, RAHMAN A, YAVARI E, et al. Identity authentication of OSA patients using microwave doppler radar and machine learning classifiers[C]. 2020 IEEE Radio and Wireless Symposium (RWS), San Antonio, USA, 2020: 251–254.
[106]	ARSALAN M, SANTRA A, and WILL C. Improved contactless heartbeat estimation in FMCW radar via Kalman filter tracking[J]. IEEE Sensors Letters, 2020, 4(5): 1–4. doi: 10.1109/LSENS.2020.2983706
[107]	WANG Qisong, DONG Zhening, LIU Dan, et al. Frequency-modulated continuous wave radar respiratory pattern detection technology based on multifeature[J]. Journal of Healthcare Engineering, 2021, 2021: 9376662. doi: 10.1155/2021/9376662
[108]	BAHAMMAM A S, TATE R, MANFREDA J, et al. Upper airway resistance syndrome: Effect of nasal dilation, sleep stage, and sleep position[J]. Sleep, 1999, 22(5): 592–598. doi: 10.1093/sleep/22.5.592
[109]	GU Weixi, SHANGGUAN Longfei, YANG Zheng, et al. Sleep hunter: Towards fine grained sleep stage tracking with smartphones[J]. IEEE Transactions on Mobile Computing, 2016, 15(6): 1514–1527. doi: 10.1109/TMC.2015.2462812
[110]	MENON A and KUMAR M. Influence of body position on severity of obstructive sleep apnea: A systematic review[J]. International Scholarly Research Notices, 2013, 2013: 670381. doi: 10.1155/2013/670381
[111]	UCHINO K, SHIRAISHI M, TANAKA K, et al. Impact of inability to turn in bed assessed by a wearable three-axis accelerometer on patients with Parkinson’s disease[J]. PLoS ONE, 2017, 12(11): e0187616. doi: 10.1371/journal.pone.0187616
[112]	LIEBENTHAL J A, WU Shasha, ROSE S, et al. Association of prone position with sudden unexpected death in epilepsy[J]. Neurology, 2015, 84(7): 703–709. doi: 10.1212/WNL.0000000000001260
[113]	KLOSTER R and ENGELSKJØN T. Sudden unexpected death in epilepsy (SUDEP): A clinical perspective and a search for risk factors[J]. Journal of Neurology, Neurosurgery & Psychiatry, 1999, 67(4): 439–444. doi: 10.1136/jnnp.67.4.439
[114]	ZHOU Tao, XIA Zhaoyang, WANG Xiangfeng, et al. Human sleep posture recognition based on millimeter-wave radar[C]. 2021 Signal Processing Symposium (SPSympo), LODZ, Poland, 2021: 316–321.
[115]	ADIB F, HSU C Y, MAO Hongzi, et al. Capturing the human figure through a wall[J]. ACM Transactions on Graphics (TOG) , 2015, 34(6): 1–13. doi: 10.1145/2816795.2818072
[116]	ZHAO Mingmin, LI Tianhong, ABU ALSHEIKH M, et al. Through-wall human pose estimation using radio signals[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7356–7365.
[117]	ZHAO Mingmin, TIAN Yonglong, ZHAO Hang, et al. RF-based 3D skeletons[C]. The 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary, 2018: 267–281.
[118]	YUE Shichao, YANG Yezhe, WANG Hao, et al. BodyCompass: Monitoring sleep posture with wireless signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(2): 1–25. doi: 10.1145/3397311
[119]	RUBENSTEIN L Z. Falls in older people: Epidemiology, risk factors and strategies for prevention[J]. Age and Ageing, 2006, 35(S2): ii37–ii41. doi: 10.1093/ageing/afl084
[120]	HOPKINS J. Falls Cost U. S. Hospitals $34 billion in direct medical costs[EB/OL]. https://www.johnshopkinssolutions.com/article/falls-cost-u-s-hospitals-30-billion-in-direct-medical-costs/, 2015.
[121]	KANNUS P, SIEVÄNEN H, PALVANEN M, et al. Prevention of falls and consequent injuries in elderly people[J]. The Lancet, 2005, 366(9500): 1885–1893. doi: 10.1016/S0140-6736(05)67604-0
[122]	FLEMING J and BRAYNE C. Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90[J]. BMJ, 2008, 337: a2227. doi: 10.1136/bmj.a2227
[123]	MUDRAZIJA S, ANGEL J L, CIPIN I, et al. Living alone in the United States and Europe: The impact of public support on the independence of older adults[J]. Research on Aging, 2020, 42(5/6): 150–162. doi: 10.1177/0164027520907332
[124]	GURBUZ S Z and AMIN M G. Radar-based human-motion recognition with deep learning: Promising applications for indoor monitoring[J]. IEEE Signal Processing Magazine, 2019, 36(4): 16–28. doi: 10.1109/MSP.2018.2890128
[125]	JOKANOVIĆ B and AMIN M. Fall detection using deep learning in range-Doppler radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 180–189. doi: 10.1109/TAES.2017.2740098
[126]	元志安, 周笑宇, 刘心溥, 等. 基于RDSNet的毫米波雷达人体跌倒检测方法[J]. 雷达学报, 2021, 10(4): 656–664. doi: 10.12000/JR21015 YUAN Zhian, ZHOU Xiaoyu, LIU Xinpu, et al. Human fall detection method using millimeter-wave radar based on RDSNet[J]. Journal of Radars, 2021, 10(4): 656–664. doi: 10.12000/JR21015
[127]	WANG Bo, GUO Liang, ZHANG Hao, et al. A millimetre-wave radar-based fall detection method using line kernel convolutional neural network[J]. IEEE Sensors Journal, 2020, 20(22): 13364–13370. doi: 10.1109/JSEN.2020.3006918
[128]	TIAN Yonglong, LEE G H, HE Hao, et al. RF-based fall monitoring using convolutional neural networks[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3): 137. doi: 10.1145/3264947
[129]	DENG Muqing, WANG Cong, TANG Min, et al. Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification[J]. Neural Networks, 2018, 100: 70–83. doi: 10.1016/j.neunet.2018.01.009
[130]	OJAROUDI M and BILA S. Multiple time-variant targets detection using MIMO radar framework for cerebrovascular monitoring[C]. 2021 15th European Conference on Antennas and Propagation (EuCAP), Dusseldorf, Germany, 2021: 1–5.
[131]	HUANG Kewu, YANG Ting, XU Jianying, et al. Prevalence, risk factors, and management of asthma in China: A national cross-sectional study[J]. The Lancet, 2019, 394(10196): 407–418. doi: 10.1016/S0140-6736(19)31147-X
[132]	WANG Chen, XU Jianying, YANG Lan, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health study): A national cross-sectional study[J]. The Lancet, 2018, 391(10131): 1706–1717. doi: 10.1016/S0140-6736(18)30841-9
[133]	VARON C, MORALES J, LÁZARO J, et al. A comparative study of ECG-derived respiration in ambulatory monitoring using the single-lead ECG[J]. Scientific Reports, 2020, 10(1): 5704. doi: 10.1038/s41598-020-62624-5
[134]	HA U, MADANI S, and ADIB F. WiStress: Contactless stress monitoring using wireless signals[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021, 5(3): 1–37. doi: 10.1145/3478121
[135]	BAI Yang, GUAN Yu, and NG Wanfai. Fatigue assessment using ECG and actigraphy sensors[C]. The 2020 International Symposium on Wearable Computers, Virtual Event, Mexico, 2020: 12–16.
[136]	DE VRIES J, MICHIELSEN H, VAN HECK G L, et al. Measuring fatigue in sarcoidosis: The Fatigue Assessment Scale (FAS)[J]. British Journal of Health Psychology, 2004, 9(3): 279–291. doi: 10.1348/1359107041557048
[137]	LIU Jie, ZHANG Kai, HE Wei, et al. Non-contact human fatigue assessment system based on millimeter wave radar[C]. 2021 IEEE 4th International Conference on Electronics Technology (ICET), Chengdu, China, 2021: 173–177.

施引文献

资源附件(0)

访问统计

图(3) / 表(2)

计量

文章访问数:
HTML全文浏览量:
PDF下载量:
被引次数: 0

1. 引言
2. GNSS-InSAR场景数据采集优化模型
2.1 GNSS-InSAR相干系数理论模型
2.2 数据采集优化模型
3. 实验验证
3.1 GNSS-InSAR场景3维形变反演实验设计
3.2 重轨数据采集时间优化
3.3 结果分析
4. 结论

基于FMCW雷达的非接触式医疗健康监测技术综述

DOI: 10.12000/JR22019 CSTR: 32380.14.JR22019

通讯作者:
方震 zfang@mail.ie.ac.cn

计量