COVID 19 – Monitoring with IoT Devices

Corona is a single-stranded RNA virus that has been around since the late 1960s when it was first discovered. The Nidovirales order includes the Corona viridae family of viruses. The crown-shaped spikes on the virus structure's outer surface inspire the name Corona. The virus has affected chickens and pigs, but there hasn't been a significant human-to-human transmission. The virus's mode of communication and other related information are continually updated every few weeks, increasing uncertainty. A Chinese study suggests that the COVID-19 pandemic had a significant psychological impact on more than half of the participants. One more ongoing review from Denmark revealed mental prosperity as adversely impacted. According to the American Psychiatric Association's survey, nearly half of Americans were anxious. The chapter details the disease, its symptoms and measures taken. 

Numerous domains now employ learning algorithms. It has distinct performance metrics appropriate for them.. Based on a predetermined set of paired input-output training samples, a machine learning paradigm known as “Supervised Learning” is used to gather information about a system's input-output relationship. An input-output training sample is also known as supervised or labeled training data because the output is regarded as the input data or supervision label. Supervised learning aims to build an artificial system that can learn the mapping between input and output and predict the system's output, given new information. The learned mapping results in the classification of the input data if the output takes a limited set of discrete values representing the input's class labels. Regression of the information occurs if the output takes continuous values. The chapter details the various algorithms, technologies used and their applications.

Semi-supervised learning, or SSL, falls somewhere between supervised and unsupervised learning. The algorithm is provided with some supervision data in addition to unlabeled data. There are two primary learning paradigms in it. Transductive education aims to use the trained classifier on unlabeled instances observed during training. This kind of algorithm is mainly used for node embedding on graphs, like random walks, where the goal is to label the graph's unlabeled nodes at the training time. Inductive learning aims to develop a classifier that can generalize unobserved situations during a test. This chapter details different semi-supervised algorithms in healthcare.

The broad term “health care” refers to a system that focuses on improving medical services to meet the needs of patients. Patients, doctors, vendors, health companies, and IT companies all work to keep and restore health records in the healthcare industry. It uses machine learning. Healthcare analysis addresses a variety of diseases, including cancer, diabetes, stroke, and others. Both the labeled value and the target value are known. Training the data for unsupervised learning is also involved. Because the label value is either unknown or absent, it is impossible to evaluate the model's performance in unsupervised learning. The chapter details different unsupervised algorithms. 

In December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that caused pneumonia spread to Wuhan City, Hubei Province, China. Fever, dry cough, and fatigue are typical clinical manifestations of COVID-19, frequently accompanied by pulmonary involvement. SARS-CoV-2 is highly contagious, making most people in the general population susceptible to infection. One of the most popular technologies, the Internet of Things (IoT), has much potential for combating the coronavirus outbreak. It has transformed real-world objects into sophisticated virtual ones. The Internet of Things (IoT) aims to connect everything in our world and assist users in controlling the objects in their immediate vicinity and keeping them informed of their current state. IoT devices sense the environment without human or machine interaction and send the gathered data to the Internet cloud. Tens of millions of devices are connected via the Internet of Things (IoT), and the number of connected devices is rapidly increasing. The chapter aims to highlight the role of IoT devices in detecting Covid-19. It details the different architectures of the system. Various domains, like the role of machines in healthcare, transportation, entertainment, retailing, and education, are detailed. It addresses challenges - awareness, accessibility, human power crisis, affordability, and accountability. Some of the future directions managed including edge architecture, cryptography, blockchain, machine learning, digital twin, unified network integration, context-aware accessibility, edge and fog computing, and sensor and actuator integration are summarized.