{"title":"基于频率调制连续波 MIMO 雷达的系统生命体征检测框架","authors":"Yong Wang;Heng Liu;Wei Xiang;Jiacheng Wang;Mu Zhou;Yu Pang;Dusit Niyato","doi":"10.1109/TII.2025.3545053","DOIUrl":null,"url":null,"abstract":"The frequency-modulated continuous wave (FMCW) radar has received much attention in the field of noncontact vital signs monitoring. However, since vital signs are usually very weak, it can be easily buried by interference and noise, especially for the heartbeat signal. To tackle this challenge, this article proposes a novel systematic vital signs detection framework using the multiple-input multiple-output FMCW radar. First, the signal noise ratio of the vital signs signal is enhanced by combining the phase signals of multiple channels using the maximum ratio combining method. Then, to suppress noise and interference, we construct the vital signs signal with singular spectral analysis and propose a correlation-based selection criterion to select potential intrinsic mode functions of the respiration and heartbeat signals. Finally, a fast independent component analysis is applied to extract the respiration signal, and the second-order derivative based fast independent component analysis in conjunction with an infinite impulse response notch filter is further developed to extract the heartbeat signal. Simulations and experimental results validate the effectiveness of the proposed framework.","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"21 7","pages":"5059-5068"},"PeriodicalIF":9.9000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Systematic Vital Signs Detection Framework Based on Frequency-Modulated Continuous Wave MIMO Radar\",\"authors\":\"Yong Wang;Heng Liu;Wei Xiang;Jiacheng Wang;Mu Zhou;Yu Pang;Dusit Niyato\",\"doi\":\"10.1109/TII.2025.3545053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The frequency-modulated continuous wave (FMCW) radar has received much attention in the field of noncontact vital signs monitoring. However, since vital signs are usually very weak, it can be easily buried by interference and noise, especially for the heartbeat signal. To tackle this challenge, this article proposes a novel systematic vital signs detection framework using the multiple-input multiple-output FMCW radar. First, the signal noise ratio of the vital signs signal is enhanced by combining the phase signals of multiple channels using the maximum ratio combining method. Then, to suppress noise and interference, we construct the vital signs signal with singular spectral analysis and propose a correlation-based selection criterion to select potential intrinsic mode functions of the respiration and heartbeat signals. Finally, a fast independent component analysis is applied to extract the respiration signal, and the second-order derivative based fast independent component analysis in conjunction with an infinite impulse response notch filter is further developed to extract the heartbeat signal. Simulations and experimental results validate the effectiveness of the proposed framework.\",\"PeriodicalId\":13301,\"journal\":{\"name\":\"IEEE Transactions on Industrial Informatics\",\"volume\":\"21 7\",\"pages\":\"5059-5068\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Industrial Informatics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10950124/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Informatics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10950124/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Systematic Vital Signs Detection Framework Based on Frequency-Modulated Continuous Wave MIMO Radar
The frequency-modulated continuous wave (FMCW) radar has received much attention in the field of noncontact vital signs monitoring. However, since vital signs are usually very weak, it can be easily buried by interference and noise, especially for the heartbeat signal. To tackle this challenge, this article proposes a novel systematic vital signs detection framework using the multiple-input multiple-output FMCW radar. First, the signal noise ratio of the vital signs signal is enhanced by combining the phase signals of multiple channels using the maximum ratio combining method. Then, to suppress noise and interference, we construct the vital signs signal with singular spectral analysis and propose a correlation-based selection criterion to select potential intrinsic mode functions of the respiration and heartbeat signals. Finally, a fast independent component analysis is applied to extract the respiration signal, and the second-order derivative based fast independent component analysis in conjunction with an infinite impulse response notch filter is further developed to extract the heartbeat signal. Simulations and experimental results validate the effectiveness of the proposed framework.
期刊介绍:
The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.