An Intelligent Health Control Security Robotic System
Keywords:
Face Detection, Facemask Detection, Audio Features, RobotAbstract
Nowadays, scientific knowledge is constantly bringing comfort and change to everyday life but the entire world is facing a major health crisis as a result of coronavirus disease transmission. According to the World Health Organization (WHO), wearing a mask on the face in public areas is one effective method of protection against COVID. Autonomous robots have become a prominent technology in recent years, with applications in a variety of fields. Robots are utilized to complete tasks more quickly than humans. Generally, robots are smarter with endless energy levels, and are precise in task management. Therefore, propose the methodology of this work is focused on developing a robot-based COVID-19 protection system. The proposed robot-based COVID-19 protection system consists of five core steps: 1) Person Identification, 2) Vaccination Checking 3) Face Recognition 4) Face Mask Detection, and 5) Temperature Checking. Person identification is performed using HOG to detect faces and machine learning classifier SVM for identification of the person. Then vaccination status is checked. To check vaccination status, it gets the name from face recognition and matches with the names in the database and gets a value from the vaccination column to show if the person is vaccinated or not and facemask detection is performed by facemask detector using Keras Mobile-Net architecture. The temperature is checked using the MLX90614 temperature sensor. The robot performs all these functions by speaking and by displaying them on the LCD screen. Artificial Intelligence on basis of deep learning and neural networks could help in fighting Corona Virus in many ways. The purpose of this system is to use a security robot instead of a security guard in organizations such as universities, colleges, schools, offices, software houses, and other organizations. The proposed system performed better compared to the existing systems as it achieves 99% precision and a 0.01% error rate.
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