Muhammad Muqtadir, M. H. Butt, Daniyal Qazi, Faran Awais Butt, I. Naqvi, N. Hassan
{"title":"Health Secure Radar: Use of Micro Doppler Signatures for Health Care and Security Applications","authors":"Muhammad Muqtadir, M. H. Butt, Daniyal Qazi, Faran Awais Butt, I. Naqvi, N. Hassan","doi":"10.1109/APWCS50173.2021.9548761","DOIUrl":null,"url":null,"abstract":"Microwave-based radar sensors are increasingly been used for healthcare and security applications. The software defined implementation of the radars allows fall detection and classification of different types of motions enabling elderly care and monitoring without privacy invading cameras. In addition, such radar sensor allow seeing through visually opaque materials suitable for security applications. This paper investigates the use of micro-Doppler signatures of slowly moving objects to localize and detect and classify human micro-motions. Using the NI SDRs, we measure micro-Doppler signatures of various human motion scenarios. Thereafter, the micro Doppler signatures’ data is augmented before being used to train a convolutional neural network that detects and identifies the fall events.","PeriodicalId":164737,"journal":{"name":"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE VTS 17th Asia Pacific Wireless Communications Symposium (APWCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APWCS50173.2021.9548761","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Microwave-based radar sensors are increasingly been used for healthcare and security applications. The software defined implementation of the radars allows fall detection and classification of different types of motions enabling elderly care and monitoring without privacy invading cameras. In addition, such radar sensor allow seeing through visually opaque materials suitable for security applications. This paper investigates the use of micro-Doppler signatures of slowly moving objects to localize and detect and classify human micro-motions. Using the NI SDRs, we measure micro-Doppler signatures of various human motion scenarios. Thereafter, the micro Doppler signatures’ data is augmented before being used to train a convolutional neural network that detects and identifies the fall events.