scholarly journals Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

Author(s):  
Feng Jin ◽  
Renyuan Zhang ◽  
Arindam Sengupta ◽  
Siyang Cao ◽  
Salim Hariri ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julius Žilinskas ◽  
Algirdas Lančinskas ◽  
Mario R. Guarracino

AbstractDuring the COVID-19 pandemic it is essential to test as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal and nasopharyngeal swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. One of the ways to speed up the testing procedures is a group testing, where the swabs of multiple patients are grouped together and tested. In this paper we propose to use the group testing technique in conjunction with an advanced replication scheme in which each patient is allocated in two or more groups to reduce the total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13 ×  average reduction of tests can be achieved compared to individual testing without delay in time.


Author(s):  
Hajra Binte Naeem ◽  
Muhammad Haroon Yousaf ◽  
Farhan Hassan Khan ◽  
Amanullah Yasin

Author(s):  
Wilbert G. Aguilar ◽  
Marco A. Luna ◽  
Hugo Ruiz ◽  
Julio F. Moya ◽  
Marco P. Luna ◽  
...  

Author(s):  
I KOMANG YOGI MAHARDIKA ◽  
Bambang Guruh Irianto ◽  
Torib Hamzah ◽  
Shubhrojit Misra

Central patient monitor that is not real-time and continues will cause inaccuracies monitoring results and also sending data that is still using cable will cause limited distance. The purpose of this research is to design a central monitoring based personal computer via Xbee Pro. The contribution of this research is,  the system works in real-time and continues, more parameters, using wireless, longer transmission distances. So that monitoring can be done in real-time and continue via wireless with more distance, then the wireless system uses the Xbee Pro module which has larger output power and uses the same number of wireless modules between transmitter and receiver. Body temperature was measured using the LM35 sensor and oxygen saturation in the blood was measured using the MAX30100 sensor. Data is sent using Xbee Pro and displayed on a personal computer. At the distance of receiving data approximately 25 meters with a wall divider, obtained results of smooth monitoring without any loss of data. The results showed that the average SpO2 error value was 0.34% in module 1 and 0.68% in module 2. The average value of body temperature error was 0.46% in module 1 and 0.72% in module 2. The results of this research can be implemented in a centralized patient monitoring system at the hospital, making it easier for health workers to monitor multiple patients, with the results of monitoring in real-time and continue, more parameters, via wireless with greater distance.


Author(s):  
Julius Žilinskas ◽  
Algirdas Lančinskas ◽  
Mario R. Guarracino

AbstractIn absence of a vaccine or antiviral drugs for the COVID-19 pandemic, it becomes urgent to test for positiveness to the virus as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal (OP) and nasopharyngeal (NP) swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. To speed up the testing procedures, some attempts have been done on group testing, which means that the swabs of multiple patients are grouped together and tested. Here we propose to use this technique in conjunction with a combinatorial replication scheme in which each patient is allocated in two or more groups to reduce total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13× average reduction of tests can be achieved.


2020 ◽  
Vol 31 (7-8) ◽  
Author(s):  
Zheyi Fan ◽  
Jianyuan Yin ◽  
Yu Song ◽  
Zhiwen Liu

2019 ◽  
Vol 32 (10) ◽  
pp. 5471-5481 ◽  
Author(s):  
Juan Wang ◽  
Nan Wang ◽  
Lihua Li ◽  
Zhenhui Ren

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