IoT-Based Smart Mask Protection against the Waves of COVID-19

Abstract

In the year 2020, the word “pandemic” has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a “Pandemic”. The outbreak pattern differs widely across the globe based on the findings discovered so far; however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images; the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology; therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytics. The manuscript and associated research work integrate the IoT and Internet of Everything (IoE) based analytics with sensor technologies with real-time data so that the overall predictions will be more accurate and integrated with the health sector. Supplementary Information The online version contains supplementary material available at 10.1007/s12652-022-04395-7.

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

“…One significant challenge is the potential computational burden, especially when dealing with extensive and intricate healthcare datasets, leading to increased processing times and resource demands. The effectiveness of GAs relies heavily on appropriate parameter tuning and the selection of suitable genetic operators, a task that can be intricate and require domain‐specific expertise 91 . The inherent complexity of GA may hinder their real‐time applicability in dynamic healthcare environments where prompt decision‐making is crucial.…”

“…The effectiveness of GAs relies heavily on appropriate parameter tuning and the selection of suitable genetic operators, a task that can be intricate and require domain-specific expertise. 91 The inherent complexity of GA may hinder their real-time applicability in dynamic healthcare environments where prompt decision-making is crucial. Moreover, the lack of inherent interpretability in the solutions generated by GAs poses challenges in healthcare contexts that prioritize transparent and comprehensible decision-making processes.…”

IOT-Based Smart Helmet for COVID-19 Detection and Management

“…During the Ebola pandemic, the US authority used non-invasive IRT to screen patients for signs of Ebola at the airports (Chen et al, 2020). Goar et al (2022) proposed an IoT-enabled smart mask to detect and identify individuals with high temperatures. The smart mask was fitted with a thermal camera, an optical camera, and a microcontroller processor.…”

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

“…Nevertheless, the manual monitoring method not only wastes manpower but also can easily cause infection. Contactless mask recognition can avoid the waste of human resources and the risk of exposure, which has been highly valued by many researchers [ 2 , 3 , 4 ].…”