Lorenz Dirksmeyer, Aly Marnach, Daniel Schmiech, A. Diewald
{"title":"用FMCW雷达监测坐着人心跳和呼吸频率的算法开发","authors":"Lorenz Dirksmeyer, Aly Marnach, Daniel Schmiech, A. Diewald","doi":"10.5194/ars-19-195-2021","DOIUrl":null,"url":null,"abstract":"Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.\n","PeriodicalId":45093,"journal":{"name":"Advances in Radio Science","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Developing of Algorithms Monitoring Heartbeat and Respiration Rate of a Seated Person with an FMCW Radar\",\"authors\":\"Lorenz Dirksmeyer, Aly Marnach, Daniel Schmiech, A. Diewald\",\"doi\":\"10.5194/ars-19-195-2021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.\\n\",\"PeriodicalId\":45093,\"journal\":{\"name\":\"Advances in Radio Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Radio Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/ars-19-195-2021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Radio Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/ars-19-195-2021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Developing of Algorithms Monitoring Heartbeat and Respiration Rate of a Seated Person with an FMCW Radar
Abstract. With a radar working in the 24 GHz ISM-band in a frequency modulated continuous wave mode the major vital signs heartbeat and respiration rate are monitored. The observation is hereby contactless with the patient sitting straight up in a distance of 1–2 m to the radar. Radar and sampling platform are components developed internally in the university institution. The communication with the radar is handled with MATLAB via TCP/IP. The signal processing and real-time visualization is developed in MATLAB, too. Cornerstone of this publication are the wavelet packet transformation and a spectral frequency estimation for vital sign calculation. The wavelet transformation allows a fine tuning of frequency subspaces, separating the heartbeat signal from the respiration and more important from noise and other movement. Heartbeat and respiration are monitored independently and compared to parallel recorded ECG-data.