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Licensed Unlicensed Requires Authentication Published by De Gruyter 2021

Real-time patient health monitoring

From the book Computational Intelligence and Predictive Analysis for Medical Science

  • Shubham Sharma and Naincy Chamoli

Abstract

There are lots of factors involved in sustaining a good lifestyle and the most important among them is good health. A good health will increase the effectiveness and efficiency of human productivity throughout their life. It has been observed that in this tumultuous twenty-first century, lots of services are added to the human lifestyle and they facilitates human life by offering ease of doing work, but this also makes humans indolent because a very small number of physical activities are involved in our day-to-day regular activities. We are living in a digital world where most of the things are automated, which we can operate without doing much physical activity. It has been reported by the World Health Organization (WHO) that more people are diagnosed with chronic diseases than acute diseases. Chronic diseases are defined as disease that normally last for 3 months or longer and may get worse with time. These are diseases that develop in the human body gradually, and after some time, they may worsen the human health. Therefore, real-time health monitoring comes into picture, where, with the help of available technologies, tools and processes, we can build a framework that can help humans to monitor their health condition and alert the person if any abnormality is observed in the data collected about the human body. The way technology can be used is immensely important because, if this can be used for the right cause in the right way, it can change the life of billions of people and help them with better health, which impacts their whole life. In this chapter, we will be discussing the different technologies that can be used to build a framework for Real-Time Health Monitoring. The Internet of technologies can be used to collect data from the human body and transmit it over the Internet to the cloud, where cloud computing can be used to store the data. Machine learning and deep learning algorithms can be used for predicting if the person is diagnosed with a disease or not. Analysis tools can also be used to find abnormality in the data. Cloud computing comes into the market with lots of advantages, but it also has some disadvantages, such as security and latency, which can be resolved by adding another layer between cloud computing and edge devices, known as fog computing. In this chapter, we discuss the different types of diseases that are related to human health and how they can be monitored in real-time using the available technologies, tools and processes.

© 2021 Walter de Gruyter GmbH, Berlin/Boston
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