Smart Covid Safety Measures Checker Using Embedded Systems and Machine Learning

A. Samydurai*, S. Sri Aparna **, M. Subhashri ***, V. Sudharsan ****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Kanchipuram, Tamil Nadu, India.
Periodicity:January - June'2021
World Health Organization : COVID-19 - Global literature on coronavirus disease
ProQuest Central | ID: covidwho-1527125


In this pandemic situation, everyone are vulnerable to COVID-19. So the necessity for safety measures checking system has become inevitable. Every measure is currently being checked separately using man power, crowded places are more susceptible for the spread of disease. Using infrared sensor and setting up a threshold crowd management is accomplished. The proposed system delivers a non contact solution by integrating sensors in order to monitor the human body temperature and environmental conditions remotely, automatically and quickly. The system completely avoids direct contact with the residents during the process of screening. Along with temperature sensing, proper sanitization is also critical. As a result, the proposed work also employs a pump motor to spray sanitization automatically and without the need for human intervention. Many companies and organizations must adjust to and protect an infected person by spotting someone who does not wear a masked face; however, this is not always possible. The proposed system also investigates the performance of detecting people wearing a face mask in a real-time situation using Machine Learning. The results show the highest precision from all input images and a camera.


COVID-19, Non-Contact Sensors, Machine Learning, Real-Time Face Recognition.

How to Cite this Article?

Samydurai, A., Aparna, S. S., Subhashri, M., and Sudharsan, V. (2021). Smart Covid Safety Measures Checker Using Embedded Systems and Machine Learning. i-manager's Journal on Digital Signal Processing, 9(1), 15-22.


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