Abstract
Coronavirus has infected billions of individuals worldwide, with the number of persons infected continuing to rise. Humans contract the virus through direct, indirect, or close contact with infected individuals. This proposed work introduces a new feature, an intelligent community distance system, that allows people to maintain community distances among others in the indoor and outdoor areas, to avoid exposure to COVID-19 and to delay its spread locally and internationally, to help prevent the spread of COVID-19. The proposed research intends to monitor an IoT-based portable monitoring device that is designed to measure COVID-19 signals. Furthermore, by monitoring real-time GPS data, the system automatically notifies medical authorities concerned about any confinement violations of patients who may be infected. Also, figure out what new tool will be beneficial for tracking and predicting COVID-19 collections. To support in the analysis of COVID-19, the solution incorporates a mobile system coupled with a portable device that is equipped with clever IoT capabilities (complex data analysis and intelligent data detection) embedded within the system. A comparison of various machine learning classifier algorithms such as SVM, Random Forest, KNN, and Decision Tree is presented as the best model for making predictions and determining accuracy. We observed that KNN performs better, with a 95% accuracy rate. COVID-19 will be used to prevent the spread of diseases in future global medical problems using an automatic social distance monitoring and contact tracking system.
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Kanakaprabha, S., Arulprakash, P., Priyanka, V., Varghese, V., Sureshkumar, A. (2023). IOT Based Solution for Effective Social Distancing and Contact Tracing for COVID-19 Prevention. In: Hemanth, J., Pelusi, D., Chen, J.IZ. (eds) Intelligent Cyber Physical Systems and Internet of Things. ICoICI 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-031-18497-0_46
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