A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring

计算机科学 雷达 远程病人监护 医疗保健 遥感 声纳 声传感器 系统工程 实时计算 工程类 人工智能 电信 声学 医学 物理 放射科 经济 地质学 经济增长
作者
Zhongyuan Fang,Fei Gao,Haoran Jin,Siyu Liu,Wensong Wang,Ruochong Zhang,Zesheng Zheng,Xuan Xiao,Kai Tang,Liheng Lou,Kea‐Tiong Tang,Jie Chen,Yuanjin Zheng
出处
期刊:IEEE Transactions on Biomedical Circuits and Systems [Institute of Electrical and Electronics Engineers]
卷期号:16 (6): 1075-1094 被引量:29
标识
DOI:10.1109/tbcas.2022.3226290
摘要

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.
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