Prevention Equipment for COVID-19 Spread Using IoT and Multimedia-Based Solutions

The global spread of COVID-19 is a growing concern for everyone. The virus is transmitted through droplets and airborne particles from one person to another. The World Health Organization (WHO) recommends wearing a face mask, social distancing, avoiding crowded areas, and maintaining a strong immune system to reduce the spread of COVID-19. In response to the pandemic, many countries have implemented lockdowns to control its spread. Research has shown that wearing masks in public can help prevent person-to-person transmission of the virus. This paper proposes a device that uses cameras to detect elevated body temperature, people wearing face masks, those not wearing face masks, and calculates proximity among individuals. The proposed model can be deployed in public places such as shopping malls, hotels, apartment entrances, airports, hospitals, and offices to maintain safety standards. The system uses Internet of Things (IoT) technology and deep learning mechanisms to detect individuals who may be infected with COVID-19. The proposed framework is evaluated using the face mask detection and social distance detecting algorithms in the TensorFlow library. A non-contact sensor is used to check the temperature of each person passing through the device. To ensure ease of use, an animated film is used to help people understand how to operate the proposed system. A multimedia application is also employed to display the system’s output to end-users in the form of visualizations or reports, accompanied by an alarming sound to remind individuals to maintain distance or avoid crowded areas. The proposed system, when implemented, can help prevent the spread of COVID-19 and save lives.