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IOT Based Health Monitoring Automated Predictive System to Confront Covid-19

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dc.contributor.author Choyon, Md Mashrur Sakib
dc.contributor.author Rahman, Maksudur
dc.contributor.author Kabir, Md. Mohsin
dc.contributor.author Mridha, M. F.
dc.date.accessioned 2021-11-29T04:11:26Z
dc.date.available 2021-11-29T04:11:26Z
dc.date.issued 2020-01
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6483
dc.description.abstract As the whole world is striving to combat the Coronavirus disease (COVID-19), healthcare and health monitoring systems are struggling to confront the virus. Many cases have been observed where the COVID-19 could not be identified at a specific time. Furthermore, any effective strategy that can monitor the coronavirus state in the human body has not been established yet. As a result, patients of the coronavirus could not receive proper treatment when necessary. Therefore, the death toll due to COVID-19 is rising. This paper proposes a systematic approach to combat the COVID-19 pandemic more efficiently by combining the concept of ‘Internet of Things’ (IOT) and machine learning (ML). The paper also gives a brief idea about how IOT can be used to monitor the health status and also to detect the severity of coronavirus in a human body by using some of the biological data such as body temperature, heart pulse, etc. from the patient's body. The developed system can provide healthcare, maintain distant communication, and emergency medical support to the patients. This paper proposes a practical solution with the help of the developed health monitoring system that can mitigate the loss done by the COVID-19. en_US
dc.language.iso en_US en_US
dc.publisher 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), IEEE en_US
dc.subject COVID-19 en_US
dc.subject IOT en_US
dc.subject Machine learning en_US
dc.subject Embedded multiple sensors en_US
dc.subject Health monitoring system en_US
dc.subject IOMT en_US
dc.title IOT Based Health Monitoring Automated Predictive System to Confront Covid-19 en_US
dc.type Article en_US


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