Intelligent Real-Time Face Mask and Temperature Recognition Using Deep Learning on Raspberry Pi

There has been a steady and significant growth of the advancement in computer vision systems for face masks and temperature tracking. The World Health Organization introduce strict measures to prevent the spread of the coronavirus disease. This paper attempts to create a highly accurate and real-time approach that can effectively detect non-mask trying to enforce to wear mask in order to contribute to community health. For the purpose of detecting face masks, a hybrid model combining deep and regular machine learning will be utilized. We will use OpenCV to recognize faces in real time from a live feed via the Camera module using a dataset that includes images with and without masks and send the data to the cloud for visualization and further analysis. As a main part of the solution, we proposed embedded system with tools utilizing Python, OpenCV, and Tensor Flow with using computer vision and deep learning. To make it cost efficient, quick, scalable, and effective the whole process for detection of face mask is carried out on Raspberry Pi. This project enables improved control over the information already provided and strongly points out the deployment of our method to stop the local transmission from spreading and decrease the possibility of human coronavirus disease carriers. © 2022 IEEE.

EHR system tracks prisoner's data across countries to monitor COVID-19 in U.S. State and Federal Prisons

The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of our lives. While healthcare workers fight the virus in the front line, we do our part by creating an Electronic Health Records system that tracks state and federal prisoner's data through the countries in order to monitor COVID-19 cases in the U.S. The main objective of our system is to visualize the relationship between current data of deaths per day in both, state and federal prisons in U.S. State. In order to accomplish this process, we combine COVID-19 case and death rates into a single data collection which will show that state prisons have consistently reported greater rates of COVID-19 than federal prisons. The obtained results from the process satisfied our expectations and provide the efficiency of implementing Electronic Health Records Systems. © 2022 IEEE.