Covid-19 Indoorsafety Monitoring System Using Machine Learning

Abstract

The covid-19 epidemic is causing a world pandemic crisis. The powerful device in these situations is to wear a mask in public entry, schools, and super markets to reduce the Covid-19 spread. There are many convolutions face recognition technologies to distinguish effective images for monitoring the discovery of a face mask. Therefore, it is very important to improve the effectiveness of the acquisition methods available in the existing system. The data set value increases in the proposed input to improve the maximum accuracy. The proposed method is used to determine body temperature, face mask, and social retention using advanced machine learning methods. Using the EM8RFID scanner personal data such as temperature value, face mask identification and public distance detection are collected. It is used to indicate the state of human health in a cloud platform. A wireless heat sensor issued to determine a person's body temperature using MLX90614 without anyone. The Raspberry integrated with the pi camera is used in detecting a face mask and a social distance. Raspberrypi captures the image and detects with the convolution neural network algorithm verifying a person is wearing a face mask, following social distance. Therefore, authorities should monitor the human condition in the cloud platform area. By applying this concept, the spread of Covid-19 can be greatly reduced and it is easier to identify peoplewith Covid-19symptoms.