Vital Signs Detection of Moving Targets Using FMCW Radar

雷达 遥感 连续波雷达 计算机科学 雷达探测 雷达成像 地质学 电信
作者
Xiao Dai,Yuanhui Zhang,Jingxue Luo,Kang Liu,Duo Fu
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:36 (1): 017002-017002
标识
DOI:10.1088/1361-6501/ad8470
摘要

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
蜜桃吐司完成签到 ,获得积分10
刚刚
乐正成危完成签到 ,获得积分10
刚刚
大个应助Calvin采纳,获得10
1秒前
自信白梦发布了新的文献求助10
1秒前
yangzihua完成签到,获得积分10
1秒前
1秒前
1秒前
SharonDu发布了新的文献求助20
2秒前
向聿发布了新的文献求助10
2秒前
2秒前
欢呼问旋完成签到,获得积分10
2秒前
小二郎应助FG采纳,获得10
3秒前
LRxxx完成签到 ,获得积分10
5秒前
6秒前
syr完成签到,获得积分10
6秒前
6秒前
积极的珊发布了新的文献求助20
7秒前
科研通AI5应助wxh采纳,获得10
7秒前
cc发布了新的文献求助10
7秒前
7秒前
打打应助李龙波采纳,获得10
8秒前
英俊的铭应助zzz采纳,获得10
9秒前
9秒前
9秒前
yu发布了新的文献求助10
9秒前
9秒前
友好的尔容完成签到,获得积分10
10秒前
彭于晏应助殷勤的斓采纳,获得10
10秒前
wsysweet完成签到,获得积分10
10秒前
L.G.Y发布了新的文献求助10
11秒前
11秒前
12秒前
小巧念露发布了新的文献求助10
12秒前
13秒前
lyt发布了新的文献求助10
13秒前
嘿嘿完成签到 ,获得积分20
14秒前
鹄望发布了新的文献求助10
14秒前
或无情发布了新的文献求助10
14秒前
S-Lab Sonic完成签到,获得积分10
15秒前
10086完成签到 ,获得积分10
16秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 710
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Virulence Mechanisms of Plant-Pathogenic Bacteria 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3563901
求助须知:如何正确求助?哪些是违规求助? 3137137
关于积分的说明 9421201
捐赠科研通 2837605
什么是DOI,文献DOI怎么找? 1559912
邀请新用户注册赠送积分活动 729212
科研通“疑难数据库(出版商)”最低求助积分说明 717197