Contactless Covid-19 Monitoring System Using IOT

The main purpose of this study is to track down corona virus interactions using the Internet of Things. The sickness is reported to be very contagious when it comes into touch with sick people. High fever, cough, and trouble breathing are the most common signs of COVID19. They've demonstrated how the sickness has evolved to conceal its signs. Because this sickness is highly contagious, it has the potential to spread rapidly, killing thousands of people. And the transmission chain must be identified as a top concern. The Internet of Things are collection that work together to accomplish a goal. Every object has its own identity, which will be used to record main Occurrences serve as a springboard for future learning and judgments. In the medical industry, IoT plays an indisputable role in disease identification and surveillance. A new epidemic is spreading across the globe. Amid a slew of other life-threatening illnesses Despite tight lockdown procedures, COVID-19, a respiratory syndrome virus discovered in 2019, is now posing a significant threat to countries. Conclusions - The authors of this study created a design for an IoT system that collects data from individuals via sensors and sends it to clinicians via mobile phones, computers, and other devices to predict the Covid-19 sickness. The main goal is to predict COVID-19 so that early health surveillance may be provided. Therefore, the writers are able to distinguish between the two. © 2022 IEEE.

Deep Learning-based Mixed Data Approach for COVID-19 Detection

The outbreak of COVID-19 has caused an exponential increase in mortality rate globally and has dealt a devastating blow to nations all over the world. This unforeseen calamity needs to be tackled and early detection of this disease could help in this regard. Several research studies used Chest X-rays and CT scans to detect the disease, which can be made cost-effective by using cough samples. These systems can further be refined by using multiple health parameters to provide more accurate results. In this view, this paper proposes a constructive way for the early detection of COVID-19 by considering cough samples and clinical data (Saturation of Peripheral Oxygen (SpO2) level, body temperature, heart rate, and symptoms). The dataset was collected by using a Raspberry Pi and an online questionnaire. In this paper, we put forward two approaches being Manual feature extraction and Mixed data neural networks (Multi-layer Perceptron and Convolutional Neural Networks) for efficiently handling the problem. To help the user access the system more comfortably, a mobile application was developed. The Mixed data neural networks yielded the best performance with an Area Under the Curve (AUC) score of 0.94 and an accuracy of 0.85. © 2021 IEEE.