COVID-19 Detection and Remote Tracking System Using IoT-Based Wearable Bracelet

A new type of virus was discovered in China in the year 2019, known as COVID-19. One of the main symptoms that are easy to spot is high body temperature. The recent virus outbreak necessitates infrared thermometers used for thermal screening at public places to test the body temperature. However, this protection method still lacks because it requires a significant amount of time to monitor large numbers of people's body temperatures. Moreover, direct contact with people infected with coronavirus may spread it to the person doing the screening. In addition, this method cannot detect the infection early without visiting the infected person to a screening place. This study proposed a new system for automatically detecting the coronavirus in early time through the body temperature with no human interactions using IoT-based wearable bracelets. The body temperature sensor is integrated into the wearable bracelet with IoT technology for monitoring the body temperature and reading the current bodily temperature. The system is additionally equipped with a GPS module. It can capture the location of the person automatically. Suppose the person is suffering from high body temperature. In that case, the system will send it with location through Wi-Fi module or GSM module over the internet to cloud database and notify medical officer at the same moment to do the immediate procedures for that person. Health officers use smartphone applications for monitoring and remote tracking using the application map. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-features Extraction for Automating Covid-19 Detection from Cough Sound using Deep Neural Networks

This research paper proposed a smart system based on deep learning to detect COVID-19 patient's using the cough sound. The deep neural networks are used to distinguish between different types of cough COVID-19 positive or negative coughs. The proposed system is segmented into three stages: Audio pre-processing by noise reduction, segmentation, feature extraction, classification, and model deployment. Eight features have been extracted from 1635 sound subjects: 573 COVID-19 positive and 1062 negative coughs. The feature's extracted data have trained using two models;first model Cough detection based on ANN used to distinguish if there is cough or not, the second model to detect the covid-19 using Convolutional Neural Network. The overall accuracy for both models is 98.1% for the Cough model and 98.5% for the Covid-19 model. The models were compiled after deployment to work together as a web service based on flask. Cough model receives cough sound from the mobile app or web interface and discriminates if there is cough then passe it coivd1-9 model that will analyze if cough is positive or negative.and send the result back to the mobile app. © 2022 IEEE