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IoT based Health Monitoring & Automated Predictive System to Confront COVID-19
IoT based Health Monitoring & Automated Predictive System to Confront COVID-19
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.
- College of Business Administration Latvia
- Daffodil International University Bangladesh
- American International University-Bangladesh Bangladesh
Microsoft Academic Graph classification: Coronavirus disease 2019 (COVID-19) Computer science business.industry Specific time Disease medicine.disease_cause Death toll Risk analysis (engineering) Health care Pandemic medicine business Internet of Things Coronavirus
Microsoft Academic Graph classification: Coronavirus disease 2019 (COVID-19) Computer science business.industry Specific time Disease medicine.disease_cause Death toll Risk analysis (engineering) Health care Pandemic medicine business Internet of Things Coronavirus
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!- College of Business Administration Latvia
- Daffodil International University Bangladesh
- American International University-Bangladesh Bangladesh
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.