工程科学学报.第43卷.第9期：1206-1214.2021年9月 Chinese Journal of Engineering,Vol.43,No.9:1206-1214,September 2021 https://doi.org/10.13374/j.issn2095-9389.2021.01.13.001;http://cje.ustb.edu.cn 基于小波分析和自相关计算的非接触式生理信号检测 刘璐瑶2，张森，2)，肖文标2，)☒ 1)北京科技大学自动化学院，北京1000832)北京市工业波谱成像工程技术研究中心，北京1000833)北京科技大学顺德研究生院，广 东528399 ☒通信作者，E-mail:wdxiao@ustb.edu.cn 摘要采用调频连续波(Frequency modulated continuous wave,FMCW)雷达实现非接触式生理信号检测，并提出了基于小波 分析和自相关计算(Wavelet analysis and autocorrelation computation,.WAAC)的检测方法.首先，毫米波FMCW雷达发射电磁 波信号，并接收来白身体的反射信号，然后，通过信号预处理从中频信号中提取包含呼吸和心跳的相位信息，消除直流偏置 并完成相位解缠.最后，基于小波包分解(Wavelet packet decomposition,.WPD)从原始信号中得到心跳和呼吸信号，利用自相 关计算减小杂波对心跳信号的影响.进而提取高精度的心率参数.应用FMCW雷达对10名受试者进行实验测试.结果表明 本文方法得到的呼吸和心率的平均绝对误差率平均值分别小于1.65%和1.83% 关键词非接触式生理信号检测：心跳检测：呼吸检测：小波分析：自相关计算：调频连续波雷达 分类号TP274.2 Noncontact vital signs detection using joint wavelet analysis and autocorrelation computation LIU Lu-yao2),ZHANG Sen'2),XIAO Wen-dong2 1)School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China 2)Beijing Engineering Research Center of Industrial Spectrum Imaging,Beijing 100083,China 3)Shunde Graduate School,University of Science and Technology Beijing,Guangdong 528399,China Corresponding author,E-mail:wdxiao @ustb.edu.cn ABSTRACT Vital signs are important parameters for human health status assessment,and timely,accurate detection is of great significance for modern health care and intelligent medical applications.Detecting vital signs,such as heartbeat and respiration signals, provides a variety of diseases with reliable diagnosis and effective prevention.Conventional contact detection may restrict the behaviors of users,cause additional burdens,and render users uncomfortable.In recent years,noncontact detection technology has successfully achieved remote long-term detection for respiration and heartbeat signals.Compared to conventional contact-detection approaches, noncontact heartbeat and respiration detection using a millimeter-wave radar is preferable as it causes no disturbance to the subject, bringing a comfortable experience,and detects vital signs under natural conditions.However,noncontact vital signs detection is challenging owing to environmental noise.Especially,heartbeat signals are very weak and are merged with respiration harmonics and environmental noise,and their extraction and recognition are even more difficult.This paper applied a frequency-modulated continuous wave(FMCW)radar to detect vital signs.The study also presented a noncontact heartbeat and respiration signals detection approach based on wavelet analysis and autocorrelation computation (WAAC).The millimeter-wave FMCW radar first transmited the electromagnetic signal and received the reflected echo signals from the human body.Thereafter,the phase information of the 收稿日期：2021-01-13 基金项目：国家重点研发计划课题资助项目(2017YFB1401203):佛山市科技创新专项资助项目(BK20AF005)基于小波分析和自相关计算的非接触式生理信号检测 刘璐瑶1,2)，张    森1,2)，肖文栋1,2,3) 苣 1) 北京科技大学自动化学院，北京 100083    2) 北京市工业波谱成像工程技术研究中心，北京 100083    3) 北京科技大学顺德研究生院，广 东 528399 苣通信作者，E-mail: wdxiao@ustb.edu.cn 摘    要    采用调频连续波（Frequency modulated continuous wave, FMCW）雷达实现非接触式生理信号检测，并提出了基于小波 分析和自相关计算（Wavelet analysis and autocorrelation computation, WAAC）的检测方法. 首先，毫米波 FMCW 雷达发射电磁 波信号，并接收来自身体的反射信号. 然后，通过信号预处理从中频信号中提取包含呼吸和心跳的相位信息，消除直流偏置 并完成相位解缠. 最后，基于小波包分解（Wavelet packet decomposition, WPD）从原始信号中得到心跳和呼吸信号，利用自相 关计算减小杂波对心跳信号的影响，进而提取高精度的心率参数. 应用 FMCW 雷达对 10 名受试者进行实验测试，结果表明 本文方法得到的呼吸和心率的平均绝对误差率平均值分别小于 1.65% 和 1.83%. 关键词    非接触式生理信号检测；心跳检测；呼吸检测；小波分析；自相关计算；调频连续波雷达 分类号    TP274.2 Noncontact  vital  signs  detection  using  joint  wavelet  analysis  and  autocorrelation computation LIU Lu-yao1,2) ，ZHANG Sen1,2) ，XIAO Wen-dong1,2,3) 苣 1) School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China 2) Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China 3) Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China 苣 Corresponding author, E-mail: wdxiao@ustb.edu.cn ABSTRACT    Vital  signs  are  important  parameters  for  human  health  status  assessment,  and  timely,  accurate  detection  is  of  great significance for modern health care and intelligent medical applications. Detecting vital signs, such as heartbeat and respiration signals, provides a variety of diseases with reliable diagnosis and effective prevention. Conventional contact detection may restrict the behaviors of users, cause additional burdens, and render users uncomfortable. In recent years, noncontact detection technology has successfully achieved  remote  long-term  detection  for  respiration  and  heartbeat  signals.  Compared  to  conventional  contact-detection  approaches, noncontact  heartbeat  and  respiration  detection  using  a  millimeter-wave  radar  is  preferable  as  it  causes  no  disturbance  to  the  subject, bringing  a  comfortable  experience,  and  detects  vital  signs  under  natural  conditions.  However,  noncontact  vital  signs  detection  is challenging owing to environmental noise. Especially, heartbeat signals are very weak and are merged with respiration harmonics and environmental noise, and their extraction and recognition are even more difficult. This paper applied a frequency-modulated continuous wave (FMCW) radar to detect vital signs. The study also presented a noncontact heartbeat and respiration signals detection approach based  on  wavelet  analysis  and  autocorrelation  computation  (WAAC).  The  millimeter-wave  FMCW  radar  first  transmited  the electromagnetic  signal  and  received  the  reflected  echo  signals  from  the  human  body.  Thereafter,  the  phase  information  of  the 收稿日期: 2021−01−13 基金项目: 国家重点研发计划课题资助项目（2017YFB1401203）；佛山市科技创新专项资助项目（BK20AF005） 工程科学学报，第 43 卷，第 9 期：1206−1214，2021 年 9 月 Chinese Journal of Engineering, Vol. 43, No. 9: 1206−1214, September 2021 https://doi.org/10.13374/j.issn2095-9389.2021.01.13.001; http://cje.ustb.edu.cn