Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, G. Fortino
{"title":"A UWB Radar-based Approach of Detecting Vital Signals","authors":"Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, G. Fortino","doi":"10.1109/BSN51625.2021.9507032","DOIUrl":null,"url":null,"abstract":"The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.","PeriodicalId":181520,"journal":{"name":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN51625.2021.9507032","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
The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.