InCOV Chamber: An IoT based Intelligent Chamber to monitor and identify potential COVID-19 positive patients

Abstract

COVID-19, the infectious disease with common symptoms such as tiredness, fever, cough, and severe symptoms such as shortness of breath has become a global pandemic that has an enormous negative impact on society. Because of the disease’s negative influence o n o r ganizational operations, the entire world is concerned about its spread within their organization. Despite the fact that fever is currently the only symptom used to identify COVID-19 suspects, there may be COVID-19 patients who may not show any signs of fever. The goal of this study is to use an IoT-based chamber to detect potential COVID-19 suspects by taking into account the aforementioned symptoms. When a person enters the chamber, our system employs Neural Networks and Artificial Intelligence (AI) to detect COVID-19 symptoms like Fever, Anosmia, Cough, and Shortness of Breath. The proposed system yields accuracies of 95% for fever detection, 96% for Anosmia detection, and 94% for cough analysis.