可重构智能超表面辅助的非视距人体生命体征感知

李新羽; 张静远; 蔡子轩; 秦世龙; 马骞; 游检卫; 崔铁军

doi:10.12263/DZXB.20240674

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可重构智能超表面辅助的非视距人体生命体征感知

中国电子学会优秀博士学位论文获得者特约专栏 | 更新时间：2025-12-08

- 可重构智能超表面辅助的非视距人体生命体征感知
- Non-Line-of-Sight Human Vital-Sign Sensing Aided by Reconfigurable Intelligent Surfaces
- 电子学报 2025年53卷第1期页码：1-13
- 作者机构：
  
  东南大学信息科学与工程学院，江苏南京 211189
- 作者简介：
  
  [ "李新羽女，1995年7月出生于辽宁省锦州市.现为东南大学信息科学与工程学院“至善”博士后.主要研究方向为智能电磁感知和生物电磁超材料.E-mail: xinyuli@seu.edu.cn" ]
  [ "张静远，女，2002年6月出生于北京市. 现为东南大学信息科学与工程学院本科生 . 主要研究方向为信息超材料，智能电磁感知. Email: zhangjingyuan@seu.edu.cn" ]
  [ "蔡子轩，男，2001年7月出生于浙江省绍兴市 . 现为东南大学信息科学与工程学院硕士研究生 . 主要研究方向是超表面的应用和智能电磁计算.E-mail: zixuancai@seu.edu.cn" ]
  [ "秦世龙，男，2001年1月出生于安徽省宿州市. 现为东南大学电磁场与微波技术专业硕士研究生. 主要研究方向为拓扑超表面及应用.E-mail: 220240824@seu.edu.cn" ]
  [ "马骞，男，1992年3月出生于浙江省绍兴市 . 现为东南大学信息科学与工程学院“至善”博士后. 主要研究方向为数字编码超材料、智能超材料、衍射神经网络 . 中国电子学会会员编号：E190027793M.E-mail: maqian@seu. edu. cn" ]
  [ "游检卫，男，1986 年 10 月出生于江西省吉安市. 现为东南大学信息科学与工程学院教授.主要研究方向为计算电磁学和电磁超材料. 中国电子学会会员编号：E190029753S.E-mail: jvyou@seu.edu.cn" ]
  [ "崔铁军，男，1965年9月出生于河北省滦平县. 现为东南大学信息科学与工程学院教授. 主要研究方向为计算电磁学和电磁超材料. 中国电子学会会员编号：E190008553F.E-mail: tjcui@seu.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2023YFB3813100);国家自然科学基金(62101124;62301149;62288101);江苏省自然科学基金(BK20210209;BK20230820);111项目(111-2-05);东南大学科研启动基金(RF1028623244)
- DOI：10.12263/DZXB.20240674
  中图分类号： TN929.533;
- 收稿：2024-07-18，
  
  修回：2024-10-29，
  
  纸质出版：2025-01-25
- 稿件说明：
移动端阅览
李新羽, 张静远, 蔡子轩, 等. 可重构智能超表面辅助的非视距人体生命体征感知[J]. 电子学报, 2025, 53(01): 1-13.

LI Xin-yu, ZHANG Jing-yuan, CAI Zi-xuan, et al. Non-Line-of-Sight Human Vital-Sign Sensing Aided by Reconfigurable Intelligent Surfaces[J]. Acta Electronica Sinica, 2025, 53(01): 1-13.
李新羽, 张静远, 蔡子轩, 等. 可重构智能超表面辅助的非视距人体生命体征感知[J]. 电子学报, 2025, 53(01): 1-13. DOI：10.12263/DZXB.20240674

LI Xin-yu, ZHANG Jing-yuan, CAI Zi-xuan, et al. Non-Line-of-Sight Human Vital-Sign Sensing Aided by Reconfigurable Intelligent Surfaces[J]. Acta Electronica Sinica, 2025, 53(01): 1-13. DOI：10.12263/DZXB.20240674

摘要

本文提出非视距场景下的无接触式人体生命体征感知方法，将可重构智能超表面与人体无线感知技术相结合，利用可重构智能超表面对电磁波的灵活调控能力，实现有障碍物遮挡情况下的人体生命体征监测.本文首先通过视觉辅助机制，利用Yolo-v7深度学习模型，实现人体目标的定位.在此基础上，可重构智能超表面根据人体位置估计结果进行编码优化，进而改变感知信号传播路径，实现非视距场景下的人体感知；同时，本文针对传统变分模态分解算法在人体生命体征感知方面的不足，进一步提出一种基于滤波的变分模态分解算法，实现人体呼吸、心跳速率的精准估计.实验结果表明，视觉辅助模块可以准确定位人体胸腔所在位置，从而使可重构智能超表面能够精准调控电磁波束，使感知信号聚焦于人体.所提出的基于滤波的变分模态分解算法可以实现人体生命体征精准估计，其呼吸和心跳平均估计误差分别为0.6 RPM和5.3 BPM.与此同时，进一步分析与讨论也证明了基于可重构智能超表面的感知方案在非视距场景下进行人体生命体征感知的有效性和准确性.

Abstract

This paper proposes a contactless human vital-sign sensing method in non-line-of-sight (NLoS) scenarios

which integrates reconfigurable intelligent surfaces (RISs) into wireless human sensing

and achieves NLoS human vital-sign sensing by utilizing the flexible manipulation capability of RISs over electromagnetic waves. Firstly

through a visual-aided mechanism

this paper locates human targets using the deep learning model Yolo-v7. Then

the metasurface optimizes the coding matrix based on the estimated human location

thereby altering the propagation path of sensing signals to achieve human sensing in non-line-of-sight scenarios. Additionally

addressing the shortcomings of traditional variational mode decomposition (VMD) algorithms in human vital-sign sensing

we further propose an improved VMD algorithm to achieve precise estimation of breathing rate and heartbeat rate. Experimental results demonstrate that the visual-aided module can accurately locate the position of human chest

thus enabling precise beamforming controlled by the intelligent metasurface. The proposed filtering-based VMD algorithm can achieve precise estimation of human vital signs

with average estimation errors of 0.6 RPM for respiration and 5.3 BPM for heartbeat

respectively. Furthermore

further analysis also demonstrates the effectiveness and accuracy of the RIS-based sensing scheme for human vital-sign sensing in NLoS scenarios.

关键词

Keywords

references

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