Review of Social Distancing and Face Mask of Coronavirus Spread

Escobedo Alcázar C.M.; Gutierrez Arias J.E.; Alvarez Cervantes N.J.; Canaza Pilco R.A.; Gonzales Medina L.E.; Monroy Barrios J.E.; Choquehuayta W.N. · CEUR Workshop Proceedings (2023)

Resumen

Development of artificial intelligence applications has let to reduce the spread of COVID-19 in many countries. The objective in this review is analysis of different solutions based on mask detection algorithms and social distancing methods to combat to COVID-19. Method applied is to search eight databases namely Ebsco, Dynamed, IEEE, IOP, Sage, Scopus, Science direct, Taylor, and Francis, and the run three sequences of search queries between 2019 and 2022. Results obtained using precise exclusion criteria and a selection strategy were applied to select the 8578 articles and then obtained 48 articles were fully assessed and included in this review, and this number only emphasized the insufficiency of research in this important area. After analyzing all the included studies, the results were distributed according to the year of publication and the commonly used deep learning and ML algorithms. The results found in all the papers were discussed to find the gaps in all the articles reviewed. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and characteristics and classes used were analyzed in detail. Conclusion is find showed that the growing emphasis on deep learning and ML techniques in field, can provide the right environment for change and improvement.

Instituciones: Universidad Tecnológica del Perú