基于毫米波雷达的智能心率提取方法

doi:10.1007/s12204-023-2656-1

摘要/Abstract

摘要： 毫米波雷达的非接触式生命体征测量技术具有重要医用价值与独特优势。然而由于心跳震动特征微弱，频率范围较广，且检测信号存在呼吸谐波及无关运动干扰等因素，进行实时鲁棒提取仍有难点。针对上述问题，将对于广范围分布的快慢心率自适应提取归纳为多尺度检测问题，将区分心跳特征与其他无关身体运动特征归纳为特征关注问题，进行了多尺度检测模块和心率特征关注模块设计，并组合为基本网络模块，搭建成心率提取神经网络。通过合理的数据集设计与模块参数设计进行实验，其结果表明，在有无关运动数据干扰的信号数据中，所提方法模型进行心率提取时的绝对误差平均可以达到1.87次/分，相对准确率平均可以达到97.51%。

关键词: 毫米波雷达, 生命体征监测, 心率, 神经网络

Abstract: The non-contact vital signs measurement technology based on millimeter wave radar has important medical value and unique advantages. However, because of its weak vibration characteristics, wide range of values, and the presence of respiratory harmonics and irrelevant motion interference in the detection signal, it is still difficult to perform a robust extraction in real time. To solve the above problems, the adaptive extraction of heart rates with a wide range of distribution is summarized as a multi-scale detection problem, and the distinction between heartbeat features and other irrelevant body motion features is summarized as a feature attention problem. Then, multi-scale detection module and heart rate feature attention module are designed and combined into a basic network module to build a heart rate extraction neural network. Through experiments based on properly designed datasets, a reasonable parameter design of the module is first explored. Experimental results show that in the signal data with unrelated motion data interference, average absolute error of the proposed method model for heart rate extraction can reach 1.87 beats/min, and average relative accuracy can reach 97.51%.

中图分类号:

TP391
R318.6

. 基于毫米波雷达的智能心率提取方法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 493-498.

Feng Lingdong, Miao Yubin. Intelligent Heart Rate Extraction Method Based on Millimeter Wave Radar[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 493-498.

参考文献

[1] HU W. Non-contact vital sign detection based on Doppler radar[D]. Hefei: University of Science and Technology of China, 2014 (in Chinese).

[2] ZHAO H, HONG H, SUN L, et al. Noncontact physiological dynamics detection using low-power digital-IF Doppler radar [J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1780-1788.

[3] LIU J, LIU H B, CHEN Y Y, et al. Wireless sensing for human activity: A survey [J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 1629-1645.

[4] PARK J Y, LEE Y G, HEO R, et al. Preclinical evaluation of noncontact vital signs monitoring using real-time IR-UWB radar and factors affecting its accuracy [J]. Scientific Reports, 2021, 11: 23602.

[5] IWATA Y, THANH H T, SUN G H, et al. High accuracy heartbeat detection from CW-Doppler radar using singular value decomposition and matched filter [J]. Sensors, 2021, 21(11): 3588.

[6] LIU J T, LI Y C, LI C Z, et al. Accurate measurement of human vital signs with linear FMCW radars under proximity stationary clutters [J]. IEEE Transactions on Biomedical Circuits and Systems, 2021, 15(6): 1393-1404.

[7] DAI T K V, OLEKSAK K, KVELASHVILI T, et al. Enhancement of remote vital sign monitoring detection accuracy using multiple-input multiple-output 77 GHz FMCW radar [J]. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2022, 6(1): 111-122.

[8] SHEN H M, XU C, YANG Y J, et al. Respiration and heartbeat rates measurement based on autocorrelation using IR-UWB radar [J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, 65(10): 1470-1474.

[9] PENG K C, SUNG M C, WANG F K, et al. Non-contact vital sign detection using gain detection technique [C]//2021 IEEE International Symposium on Radio-Frequency Integration Technology. Hualien: IEEE, 2021: 1-2.

[10] BAHMANI N, ROUVALA M, KAARNA A. Vital sign detection using short-range mm-wave pulsed radar [C]//2021 IEEE 3rd Global Conference on Life Sciences and Technologies. Nara: IEEE, 2021: 512-516.

[11] LE M, VAN NGUYEN B. Multivariate correlation of higher harmonics for heart rate remote measurement using UWB impulse radar [J]. IEEE Sensors Journal, 2020, 20(4): 1859-1866.

[12] SHIH J Y, WANG F K. Quadrature cosine transform (QCT) with varying window length (VWL) technique for noncontact vital sign monitoring using a continuous-wave (CW) radar [J]. IEEE Transactions on Microwave Theory and Techniques, 2022, 70(3): 1639-1650.

[13] XU H Q, EBRAHIM M P, HASAN K, et al. Accurate heart rate and respiration rate detection based on a higher-order harmonics peak selection method using radar non-contact sensors [J]. Sensors, 2021, 22(1): 83.

[14] SHYU K K, CHIU L J, LEE P L, et al. Detection of breathing and heart rates in UWB radar sensor data using FVPIEF-based two-layer EEMD [J]. IEEE Sensors Journal, 2019, 19(2): 774-784.

[15] HUANG T, HAYWARD L, LIN J. Adaptive harmonics comb Notch digital filter for measuring heart rate of laboratory rat using a 60-GHz radar [C]//2016 IEEE MTT-S International Microwave Symposium. San Francisco: IEEE, 2016: 1-4.

[16] XIONG Y Y, PENG Z K, GU C Z, et al. Differential enhancement method for robust and accurate heart rate monitoring via microwave vital sign sensing [J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(9): 7108-7118.

[17] MUÑOZ-FERRERAS J M, PENG Z Y, GÓMEZ-GARCÍA R, et al. Random body movement mitigation for FMCW-radar-based vital-sign monitoring [C]//2016 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems. Austin: IEEE, 2016: 22-24.

[18] RONG Y, DUTTA A, CHIRIYATH A, et al. Motion-tolerant non-contact heart-rate measurements from radar sensor fusion [J]. Sensors, 2021, 21(5): 1774.

[19] YIN W F, YANG X Z, LI L, et al. HEAR: Approach for heartbeat monitoring with body movement compensation by IR-UWB radar [J]. Sensors, 2018, 18(9): 3077.

[20] ZHAO P J, LU C X, WANG B, et al. Heart rate sensing with a robot mounted mmWave radar [C]//2020 IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020: 2812-2818.

[21] TSAI Y C, LAI S H, HO C J, et al. High accuracy respiration and heart rate detection based on artificial neural network regression [C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Montreal: IEEE, 2020: 232-235.

[22] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1-9.

[23] HU J, SHEN L, SUN G. Squeeze-and-excitation networks [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132-7141.

[24] SHI K, SCHELLENBERGER S, WILL C, et al. A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals [J]. Scientific Data, 2020, 7: 50.