Syed Aziz Shah, Syed Yaseen Shah, Syed Shah, Daniyal Haider, Ahsen Tahir, Jawad Ahmad
{"title":"利用机器学习算法利用毫米波雷达识别呼吸频率升高和浅呼吸频率","authors":"Syed Aziz Shah, Syed Yaseen Shah, Syed Shah, Daniyal Haider, Ahsen Tahir, Jawad Ahmad","doi":"10.1109/AECT47998.2020.9194198","DOIUrl":null,"url":null,"abstract":"This paper presents remote monitoring of patients using non-invasive RF sensing to detect normal respiratory rates and abnormal breathing rates such as elevated patterns where person experiences heavy breathing and shallow rates where minimal air is inhaled and exhaled. In this context, a millimeter wave, frequency modulated continuous wave radar operating at 60 GHz is used to acquire data. A total of 10 volunteers participated in the experimental campaign and 300 observations were obtained represented in terms of micro-Doppler signatures. Time domain statistical features were obtained from features such as bandwidth and centroid of the corresponding signatures. Support vector machine (SVM), k-nearest neighbor (KNN) and decision tree algorithms were used to evaluate overall performance of the proposed model. It was observed that the SVM classifier provided best classification accuracy (96%).","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Identifying Elevated and Shallow Respiratory Rate using mmWave Radar leveraging Machine Learning Algorithms\",\"authors\":\"Syed Aziz Shah, Syed Yaseen Shah, Syed Shah, Daniyal Haider, Ahsen Tahir, Jawad Ahmad\",\"doi\":\"10.1109/AECT47998.2020.9194198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents remote monitoring of patients using non-invasive RF sensing to detect normal respiratory rates and abnormal breathing rates such as elevated patterns where person experiences heavy breathing and shallow rates where minimal air is inhaled and exhaled. In this context, a millimeter wave, frequency modulated continuous wave radar operating at 60 GHz is used to acquire data. A total of 10 volunteers participated in the experimental campaign and 300 observations were obtained represented in terms of micro-Doppler signatures. Time domain statistical features were obtained from features such as bandwidth and centroid of the corresponding signatures. Support vector machine (SVM), k-nearest neighbor (KNN) and decision tree algorithms were used to evaluate overall performance of the proposed model. It was observed that the SVM classifier provided best classification accuracy (96%).\",\"PeriodicalId\":331415,\"journal\":{\"name\":\"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AECT47998.2020.9194198\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AECT47998.2020.9194198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Elevated and Shallow Respiratory Rate using mmWave Radar leveraging Machine Learning Algorithms
This paper presents remote monitoring of patients using non-invasive RF sensing to detect normal respiratory rates and abnormal breathing rates such as elevated patterns where person experiences heavy breathing and shallow rates where minimal air is inhaled and exhaled. In this context, a millimeter wave, frequency modulated continuous wave radar operating at 60 GHz is used to acquire data. A total of 10 volunteers participated in the experimental campaign and 300 observations were obtained represented in terms of micro-Doppler signatures. Time domain statistical features were obtained from features such as bandwidth and centroid of the corresponding signatures. Support vector machine (SVM), k-nearest neighbor (KNN) and decision tree algorithms were used to evaluate overall performance of the proposed model. It was observed that the SVM classifier provided best classification accuracy (96%).