毫米波传感技术的非侵入式人体生命体征检测综述

IF 3.9 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Sensor Networks Pub Date : 2023-11-03 DOI:10.1145/3627161
Yingxiao Wu, Haocheng Ni, Changlin Mao, Jianping Han, Wenyao Xu
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引用次数: 0

摘要

近年来,非侵入性人体生命体征检测因其具有非接触式、长期监测的潜力而受到广泛关注。雷达系统的进步使非接触检测人类生命体征成为可能,成为一个重要的研究领域。人体关键器官的运动影响雷达信号的传播,为研究人员提供了通过分析接收到的电磁信号来检测生命体征的机会。在这篇综述中,我们提供了当前最新的毫米波(mmWave)传感用于生命体征检测的全面概述。我们探讨了人体解剖学和各种测量方法,包括接触和非接触方法,并总结了毫米波雷达传感的原理。为了演示如何利用EM信号进行生命体征检测,我们讨论了四种基于毫米波的生命体征传感(MVSS)信号模型,并详细说明了MVSS的信号处理链。此外,我们对基于深度学习的MVSS进行了广泛的回顾,并比较了现有的研究。最后,我们对MVSS的具体应用(如生物识别认证)提供了见解,并强调了该领域未来的研究趋势。
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Non-Intrusive Human Vital Sign Detection using mmWave Sensing Technologies: A Review
Non-invasive human vital sign detection has gained significant attention in recent years, with its potential for contactless, long-term monitoring. Advances in radar systems have enabled non-contact detection of human vital signs, emerging as a crucial area of research. The movements of key human organs influence radar signal propagation, offering researchers the opportunity to detect vital signs by analyzing received electromagnetic (EM) signals. In this review, we provide a comprehensive overview of the current state-of-the-art in millimeter-wave (mmWave) sensing for vital sign detection. We explore human anatomy and various measurement methods, including contact and non-contact approaches, and summarize the principles of mmWave radar sensing. To demonstrate how EM signals can be harnessed for vital sign detection, we discuss four mmWave-based vital sign sensing (MVSS) signal models and elaborate on the signal processing chain for MVSS. Additionally, we present an extensive review of deep learning-based MVSS and compare existing studies. Finally, we offer insights into specific applications of MVSS (e.g., biometric authentication) and highlight future research trends in this domain.
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来源期刊
ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks 工程技术-电信学
CiteScore
5.90
自引率
7.30%
发文量
131
审稿时长
6 months
期刊介绍: ACM Transactions on Sensor Networks (TOSN) is a central publication by the ACM in the interdisciplinary area of sensor networks spanning a broad discipline from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It covers research contributions that introduce new concepts, techniques, analyses, or architectures, as well as applied contributions that report on development of new tools and systems or experiences and experiments with high-impact, innovative applications. The Transactions places special attention on contributions to systemic approaches to sensor networks as well as fundamental contributions.
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