IOT Based Integrated COVID-19 Self-Monitoring Tool (COV-SMT) for Quarantine

COVID-19 Self-Monitoring Tool (COV-SMT) is the research developed to address multiple issues in monitoring quarantined individuals due to COVID-19 infection. As COVID-19 is still highly infectious despite the availability of vaccines, the implementation of contactless Internet of Things (IoT) technology should be encouraged to minimize the need for medical staff to perform daily health checks and thus prevent them from being directly infected during checking. This research aims to develop an effective method to monitor quarantined individuals regarding their vital signs, such as body temperature, heart rate, and oxygen level. A contactless self-monitoring tool integrated with a stages algorithm is developed to monitor these quarantined individuals with the help of IoT technology. It can provide a consistent platform for patients or users to transfer information or data through networks, including personalized healthcare domains. COV-SMT is an effective tool to streamlet the overall process of taking measurements from quarantined individuals. It integrates multiple sensors into one tool while providing a better overall picture with its graphical presentation to help patients and medical staff better understand their health conditions. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

The importance of using the CodeInsights monitoring tool to support teaching programming in the context of a pandemic

This Innovative Practice Full Paper describes the use of a monitoring tool for teachers to assess students' performance and progress, improving their ability to make decisions and interventions in programming classes, in the context of the current COVID-19 pandemic. Considering that learning programming is not an easy task as well as the social, cultural, and educational diversity of the student population, we believe it is crucial that teachers have at their disposal up-to-date information on the learners' progress, skills, and difficulties to properly support them in gaining and maintaining a positive learning momentum. Previously, we suggested the use of this monitoring tool to supplement the information teachers can obtain through direct observation in traditional face-to-face classes. However, in the context of the current pandemic, its use takes on new significance since, in most cases, face-to-face instruction has been suppressed, demanding new strategies to collect assessment data. In this paper, we introduce some features of the system and explain how they can help teachers to support their students. Key findings from a field trial, in which the system was used for about a month and a half to support more than 60 students of an introductory programming course, are also presented. Due to COVID-19 lockdowns and stay-at-home orders, classes took place almost exclusively online via Zoom. During this time, the usage of the system enabled the teacher to monitor student progress regardless of when or where they were working. In post-experiment interviews, the teacher who participated in this study stated that using the system was vital to deal with the challenges that distance learning entails. Similarly, student feedback was also very positive. Several students mentioned that they felt more confident while using the system, knowing that the teacher was able to track their work and give them personalized feedback whenever necessary.

Artificial Neural Network based Automatic Prediction Unit for COVID 19 in Asymptomatic Patient

In this pandemic situation of COVID 19 virus attack in human race for the last few months the disease occurrence prediction and patient's condition monitoring is being a major thrust area in the global medical field by the researchers. The major problem of this disease is that the asymptomatic patient are acting as a carrier without knowledge. Which leads to a major threat in spreading the disease which even cause death in other people even after maintaining the social distancing. More over according to the research, rate of death is more in heart related disease affected patients. There is a vital need to detect cases at the early stages to minimize mortality especially in heart patients. In our work, we have designed and developed a trained Artificial Neural Network which can predict COVID 19 in asymptomatic patients and also can used for conditioning monitoring in COVID affected patients. Here we have used 2500 patients' details as training and testing data for the ANN network and 50 Symptoms as input variables. The Patients data are collected in Government Sivagangai Medical College and Hospital, Thirupathur village, Sivagangai district, Tamilnadu, India. © 2022 IEEE.

A Web-Based Application to Monitor and Inform about the COVID-19 Outbreak in Italy: The {COVID-19ita} Initiative.

The pandemic outbreak of COVID-19 has posed several questions about public health emergency risk communication. Due to the effort required for the population to adopt appropriate behaviors in response to the emergency, it is essential to inform the public of the epidemic situation with transparent data sources. The COVID-19ita project aimed to develop a public open-source tool to provide timely, updated information on the pandemic's evolution in Italy. It is a web-based application, the front end for the eponymously named R package freely available on GitHub, deployed both in English and Italian. The web application pulls the data from the official repository of the Italian COVID-19 outbreak at the national, regional, and provincial levels. The app allows the user to select information to visualize data in an interactive environment and compare epidemic situations over time and across different Italian regions. At the same time, it provides insights about the outbreak that are explained and commented upon to yield reasoned, focused, timely, and updated information about the outbreak evolution.

Novel wastewater surveillance strategy for early detection of coronavirus disease 2019 hotspots.

The novel coronavirus disease 2019, a pandemic of global concern, caused by the novel severe acute respiratory syndrome coronavirus 2 has severely revealed the need for public monitoring and efficient screening techniques. Despite the various advancements made in the medical and research field, containment of this virus has proven to be difficult on several levels. As such, it is a necessary requirement to identify possible hotspots in the early stages of any disease. Based on previous studies carried out on coronaviruses, there is a high likelihood that severe acute respiratory syndrome coronavirus 2 may also survive in wastewater. Hence, we propose the use of nanofiber filters as a wastewater pretreatment routine and upgradation of existing wastewater evaluation and treatment systems to serve as a beneficial surveillance tool.