• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于毫米波雷达的高精度生命体征检测方法。

A high precision vital signs detection method based on millimeter wave radar.

机构信息

School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361000, China.

出版信息

Sci Rep. 2024 Oct 26;14(1):25535. doi: 10.1038/s41598-024-77683-1.

DOI:10.1038/s41598-024-77683-1
PMID:39462104
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11513112/
Abstract

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

摘要

毫米波（mmWave）雷达技术在生命体征检测和医学领域有潜在的应用。为了最小化人体微运动和呼吸谐波对心率估计的影响，本文研究了基于 mmWave 雷达的生命体征检测方法。首先，我们使用中值滤波消除由人体微运动引起的基线漂移。接下来，提出了一种基于递归最小二乘自适应滤波器（RLS）和多重信号分类（MUSIC）算法相结合的差分递归最小二乘多分类（DR-MUSIC）算法。该算法有效地抑制了呼吸谐波，并分离了呼吸和心跳信号。最后，通过频谱峰值搜索可以精确估计心率值。我们邀请了一些人参与实验，结果表明该方法成功地抑制了低 SNR 下呼吸谐波的影响。估计心率与参考心率之间的误差率仅为 1.69%至 2.61%，明显优于现有算法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/f96b7aab1801/41598_2024_77683_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/224dfd376b7c/41598_2024_77683_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/5c466f0c42d0/41598_2024_77683_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/e4e320f36faf/41598_2024_77683_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/6eb958e3f350/41598_2024_77683_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/99c9fcace0da/41598_2024_77683_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/1b129a0f189f/41598_2024_77683_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/d4a09c0e5af6/41598_2024_77683_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/1904f49e4749/41598_2024_77683_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/a9d678803431/41598_2024_77683_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/8ae10abe10c4/41598_2024_77683_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/5f717fb9583e/41598_2024_77683_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/f2dc5b02decc/41598_2024_77683_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/65cf818fd2d9/41598_2024_77683_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/f96b7aab1801/41598_2024_77683_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/224dfd376b7c/41598_2024_77683_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/5c466f0c42d0/41598_2024_77683_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/e4e320f36faf/41598_2024_77683_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/6eb958e3f350/41598_2024_77683_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/99c9fcace0da/41598_2024_77683_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/1b129a0f189f/41598_2024_77683_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/d4a09c0e5af6/41598_2024_77683_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/1904f49e4749/41598_2024_77683_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/a9d678803431/41598_2024_77683_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/8ae10abe10c4/41598_2024_77683_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/5f717fb9583e/41598_2024_77683_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/f2dc5b02decc/41598_2024_77683_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/65cf818fd2d9/41598_2024_77683_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43e0/11513112/f96b7aab1801/41598_2024_77683_Fig14_HTML.jpg

相似文献

1
A high precision vital signs detection method based on millimeter wave radar.基于毫米波雷达的高精度生命体征检测方法。
Sci Rep. 2024 Oct 26;14(1):25535. doi: 10.1038/s41598-024-77683-1.
2
Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar.基于 77GHz 毫米波 FMCW 雷达的人体生命体征远程监测
Sensors (Basel). 2020 May 25;20(10):2999. doi: 10.3390/s20102999.
3
Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band.使用 120GHz 频段 FMCW 毫米波雷达进行人体生命体征的非接触式监测。
Sensors (Basel). 2021 Apr 13;21(8):2732. doi: 10.3390/s21082732.
4
Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.基于使用雷达非接触式传感器的高阶谐波峰值选择方法的精确心率和呼吸率检测
Sensors (Basel). 2021 Dec 23;22(1):83. doi: 10.3390/s22010083.
5
Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU.医学雷达时间序列信号的心电峰值提取和自适应峰值检测算法进行生命体征遥测：在健康成年人中的性能验证和在 NICU 中对新生儿监测的应用
Comput Methods Programs Biomed. 2022 Nov;226:107163. doi: 10.1016/j.cmpb.2022.107163. Epub 2022 Sep 27.
6
A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar.通过 IR-UWB 雷达对静止/非静止人体进行生命体征监测的详细算法。
Sensors (Basel). 2017 Feb 4;17(2):290. doi: 10.3390/s17020290.
7
Adaptive Separation of Respiratory and Heartbeat Signals among Multiple People Based on Empirical Wavelet Transform Using UWB Radar.基于 UWB 雷达经验模态分解的多人生理信号呼吸与心跳的自适应分离
Sensors (Basel). 2020 Aug 31;20(17):4913. doi: 10.3390/s20174913.
8
Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection.基于实时非接触毫米波雷达的生命体征检测。
Sensors (Basel). 2022 Oct 6;22(19):7560. doi: 10.3390/s22197560.
9
Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors.基于多普勒雷达传感器的生命体征检测的峰值检测算法。
Sensors (Basel). 2019 Apr 1;19(7):1575. doi: 10.3390/s19071575.
10
A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar.基于 IR-UWB 雷达的多人体生命体征非接触式检测方法。
Sensors (Basel). 2022 Aug 16;22(16):6116. doi: 10.3390/s22166116.

引用本文的文献

1
A Comprehensive Survey of Research Trends in mmWave Technologies for Medical Applications.毫米波技术在医学应用中的研究趋势综合调查
Sensors (Basel). 2025 Jun 13;25(12):3706. doi: 10.3390/s25123706.
2
Driver Head-Hand Cooperative Action Recognition Based on FMCW Millimeter-Wave Radar and Deep Learning.基于FMCW毫米波雷达和深度学习的驾驶员头部-手部协同动作识别
Sensors (Basel). 2025 Apr 10;25(8):2399. doi: 10.3390/s25082399.

本文引用的文献

1
Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar.使用相位调制连续波雷达进行非接触式活动监测的事件识别
IEEE Trans Biomed Eng. 2017 Feb;64(2):479-491. doi: 10.1109/TBME.2016.2566619. Epub 2016 May 11.
2
Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction.通过联合稀疏频谱重建实现基于光电容积脉搏波描记法的体育活动心率监测
IEEE Trans Biomed Eng. 2015 Aug;62(8):1902-10. doi: 10.1109/TBME.2015.2406332.
3
Noncontact accurate measurement of cardiopulmonary activity using a compact quadrature Doppler radar sensor.
使用紧凑型正交多普勒雷达传感器进行非接触式心肺活动精确测量。
IEEE Trans Biomed Eng. 2014 Mar;61(3):725-35. doi: 10.1109/TBME.2013.2288319. Epub 2013 Nov 4.
4
Microwave life-detection systems for searching human subjects under earthquake rubble or behind barrier.用于在地震废墟下或障碍物后搜寻人类的微波生命探测系统。
IEEE Trans Biomed Eng. 2000 Jan;47(1):105-14. doi: 10.1109/10.817625.