Human vital signs detection algorithm based on frequency modulated continuous wave radar

doi:10.11772/j.issn.1001-9081.2023060737

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (6): 1978-1986.DOI: 10.11772/j.issn.1001-9081.2023060737

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles

Human vital signs detection algorithm based on frequency modulated continuous wave radar

Mu LI¹^,², Yu LUO¹(), Xizheng KE¹^,²

^1.Faculty of Automation and Information Engineering，Xi’an University of Technology，Xi’an Shaanxi 710048，China
^2.Xi’an Key Laboratory of Wireless Optical Communication and Network Research （Xi’an University of Technology），Xi'an Shaanxi 710048，China

Received:2023-06-09 Revised:2023-08-31 Accepted:2023-09-11 Online:2023-09-20 Published:2024-06-10
Contact: Yu LUO
About author:LI Mu， born in 1972， M. S.， senior engineer. His research interests include radar signal processing， deep learning.
KE Xizheng， born in 1962， Ph. D.， professor. His research interests include wireless optical communications.
Supported by:
Xi’an Science and Technology Plan Project(2020KJRC0083)

基于调频连续波雷达的人体生命体征检测算法

李牧¹^,², 骆宇¹(), 柯熙政¹^,²

^1.西安理工大学自动化与信息工程学院, 西安 710048
^2.西安市无线光通信与网络研究重点实验室(西安理工大学), 西安 710048

通讯作者: 骆宇
作者简介:李牧（1972—），男，陕西西安人，高级工程师，硕士，主要研究方向：雷达信号处理、深度学习
柯熙政（1962—），男，陕西临潼人，教授，博士，主要研究方向：无线光通信。
基金资助:
西安市科技计划项目(2020KJRC0083)

Abstract

Abstract:

For problems such as low accuracy and poor real-time detection of existing radar non-contact vital signs detection， a human vital signs detection algorithm based on Frequency Modulated Continuous Wave （FMCW） radar was proposed. Firstly，the vital signs signal was obtained through the millimeter wave radar. Then， the adaptive decomposition and reconstruction of the vital signs signal were achieved using the improved Empirical Wavelet Transformation （EWT） algorithm. The best value of the spectrum division line was found by introducing Sparrow Search Algorithm （SSA） and Fuzzy Entropy （FE）. Finally，the heart rate and respiratory rate were calculated using the estimation algorithm with improved frequency interpolation. The superiority and robustness of the proposed algorithm were verified through comparative experiments with a medical critical care monitor. The experimental results showed that compared with Wavelet Transform （WT） algorithm， Complementary Ensemble Empirical Mode Decomposition （CEEMD） algorithm and Variational Mode Decomposition （VMD） algorithm， the Mean Square Error （MSE） was reduced by 77.65， 27.25 and 21.05， the Mean Absolute Percentage （MAPE） was reduced by 7.33， 4.33 and 3.42 percentage points， and the real-time performance was improved by 0.72 s， 16.74 s and 1.87 s. At the same time， the proposed algorithm also achieves the detection of Heart Rate Variability （HRV）.

Key words: millimeter-wave radar, Empirical Wavelet Transform (EWT), vital signs, Heart Rate Variability (HRV), Sparrow Search Algorithm (SSA)

摘要：

针对现有雷达非接触生命体征检测精度低、实时性差等问题，提出一种基于调频连续波（FMCW）雷达的人体生命体征检测算法。首先，通过毫米波雷达获取生命体征信号；其次，利用改进的经验小波变换（EWT）算法，实现生命体征信号的自适应分解和重构，通过引入麻雀搜索算法（SSA）和模糊熵（FE）寻找频谱分割线的最优值；最后，通过改进频率插值的估计算法计算心率和呼吸频率。通过与医用重症监护仪进行对比实验验证所提算法的优越性和鲁棒性。实验结果表明，所提算法相较于小波变换（WT）算法、CEEMD（Complementary Ensemble Empirical Mode Decomposition）算法和VMD（Variational Mode Decomposition）算法，均方误差（MSE）分别减小了77.65、27.25和21.05，平均绝对百分比（MAPE）分别减小了7.33、4.33和3.42个百分点，实时性分别提高了0.72 s， 16.74 s和1.87 s。同时，利用所提算法也实现了对心率变异性（HRV）的检测。

关键词: 毫米波雷达, 经验小波变换, 生命体征, 心率变异性, 麻雀搜索算法

CLC Number:

TN959.1

Mu LI, Yu LUO, Xizheng KE. Human vital signs detection algorithm based on frequency modulated continuous wave radar[J]. Journal of Computer Applications, 2024, 44(6): 1978-1986.

李牧, 骆宇, 柯熙政. 基于调频连续波雷达的人体生命体征检测算法[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1978-1986.

Figures/Tables 18

Fig.1 Schematic diagram of radar TX， RX and IF signals

Fig.2 Wavelet basis functions

Fig.3 Effect of EMD decomposition

Fig.4 Fourier spectrum segmentation diagram

Fig. 5 Effect of EWT decomposition

Fig.6 STFT effect

Fig.7 Effect of estimation algorithm with improved frequency interpolation

Tab. 1 HRV parameters and their significance

特征参数	描述	参考值/ms	异常范围/ms	异常意义
Mean_RR	RR间期的平均值	600~1 000	>1 000	心动过速
SDNN	NN间期的标准差	20~100	<20	交感神经活性减弱
RMSSD	相邻NN间期差值的均方根	20~50	<20	副交感神经活性减弱

Tab. 2 Experimental parameters

实验参数	数值	实验参数	数值
雷达调制方式	FMCW	快时间采样频率	4 MHz
起始频率	77 GHz	慢时间采样频率	32 Hz
带宽	4 GHz	检测人数	1
斜率	70 MHz/μs	受试者状态	平躺

Fig. 8 Experimental scene

Fig.9 Fitness evolutionary curves of different algorithms

Fig.10 Comparison experiment results of heart rate and respiration detection

Tab. 3 Performance comparison of three separation algorithms

算法	MSE	MAPE/%	运行时间/s
WT	82.50	9.80	1.19
CEEMD	32.10	6.80	17.21
VMD	25.90	5.89	2.34
SSA-FE-EWT	4.85	2.47	0.47

Tab. 4 Evaluation of heart rate detection results under different distance conditions

距离参数/m	准确度	均方根误差/bpm	相关系数
0.5	0.974	5.66	0.96
1.0	0.977	4.85	0.96
2.0	0.949	18.94	0.76

Tab. 5 Effectiveness validation of estimation algorithm with SSA-FE/improved frequency interpolation

实验	算法	MSE	MAPE/%	运行时间/s
实验1	SSA-FE-EWT+ 改进频率插值的估计算法	4.36	2.61	0.58
实验2	EWT+ 改进频率插值的估计算法	12.87	4.09	0.38
实验3	SSA-FE-EWT	7.92	3.11	0.47
实验4	EWT	19.52	4.97	0.27

Fig. 11 Scenario diagram of HRV comparison experiment

Fig.12 Comparison of experimental data on heartbeat interval period

Tab.6 Comparison of HRV eigenvalues

算法	$M e a n R R$	$S D N N$	$R M S S D$
ECG	758.2	25.49	21.72
本文算法	751.7	32.18	18.28
CEEMD	804.7	53.64	26.72
WT	811.8	64.42	43.25
VMD	773.0	39.84	41.32

Tab.6 Comparison of HRV eigenvalues

算法	$M e a n R R$	$S D N N$	$R M S S D$
ECG	758.2	25.49	21.72
本文算法	751.7	32.18	18.28
CEEMD	804.7	53.64	26.72
WT	811.8	64.42	43.25
VMD	773.0	39.84	41.32

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Human vital signs detection algorithm based on frequency modulated continuous wave radar

基于调频连续波雷达的人体生命体征检测算法

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