The Role of AI, IoT and Blockchain in Mitigating the Impact of COVID-19

IoT Innovation in COVID-19 Crisis

Author(s): T. Genish* and S. Vijayalakshmi

Pp: 48-68 (21)

DOI: 10.2174/9789815080650123010005

* (Excluding Mailing and Handling)

Abstract

The COVID-19 pandemic is a current global threat that surpasses provincial and radical boundaries. Due to the onset of the pandemic disease, the whole world turned entirely in a couple of weeks. Its consequences have come across the personal and professional life of human beings. The current situation focuses on precautions such as wearing a mask, maintaining social distancing, and sanitizing hands regularly. An innovative platform, and smart and effective IoT technology may be applied to follow these steps. This platform fulfills all critical challenges at the time of lockdown situations. IoT technology is more helpful in capturing real-time patient data and other essential information. IoT allows the tracing of infected people and suspicious cases and helps diagnose and treat patients remotely. It also paves the way to deliver essential medical devices and medicines to quarantined places. In the present ongoing crisis, IoT technology is inevitable in monitoring patients infected with COVID-19 through sensors and intertwined networks. The consultations are given to the patients digitally through video conferencing without meeting the medical expert in person. After the diagnosis is made digitally, IoT devices are used to track health data. Smart thermometers are used instead of traditional ones to collect valuable health data and share it with experts. The IoT robots are now a proven technology used for cleaning hospitals, disinfecting medical devices, and delivering medicines, thus giving more time to healthcare workers to treat patients.


Keywords: Agriculture, Artificial intelligence, COVID-19 Pandemic, Cardiovascular, Contact tracing, Computed tomography, Deep Learning, Digital technology, Global positioning system, Healthcare data, IoT technology, IoT robots, Perception layer, Respiration rate, Random forest, Support vector machine, Temperature, Machine learning, Magnetic resonance imaging.

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