Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic

Kasem Seresirikachorn; Paisan Ruamviboonsuk; Ngamphol Soonthornworasiri; Panisa Singhanetr; Titipakorn Prakayaphun; Natsuda Kaothanthong; Surapoom Somwangthanaroj; Thanaruk Theeramunkong

doi:10.1371/journal.pone.0281841

Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic

PLoS One. 2023 Apr 11;18(4):e0281841. doi: 10.1371/journal.pone.0281841. eCollection 2023.

Authors

Kasem Seresirikachorn¹, Paisan Ruamviboonsuk², Ngamphol Soonthornworasiri³, Panisa Singhanetr⁴, Titipakorn Prakayaphun⁵, Natsuda Kaothanthong¹, Surapoom Somwangthanaroj¹, Thanaruk Theeramunkong^{1

6}

Affiliations

¹ Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand.
² Department of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit University, Bangkok, Thailand.
³ Faculty of Tropical Medicine, Department of Tropical Hygiene, Mahidol University, Bangkok, Thailand.
⁴ Department of Ophthalmology, Mettapracharak Hospital, Nakhon Pathom, Thailand.
⁵ Department of Constructional Engineering, Graduate School of Engineering, Chubu University, Kasugai, Japan.
⁶ The Royal Society of Thailand, Bangkok, Thailand.

Abstract

Objectives: Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public's practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK.

Methods: After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis.

Results: AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13-1.17, p<0.001).

Conclusions: AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people's mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.

Copyright: © 2023 Seresirikachorn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
COVID-19*
Humans
Masks
Pandemics
Thailand

Grants and funding

We have clarified the sources of funding and stated the authors who have received salary from funders and added it to the manuscript. We also “The study was supported in part by the Thammasat University, and the Thai Research Fund (TRF) team (promotion grant number 6280015), which funded for the development of AiMask to be used by the government. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Thanaruk Theeramunkong had full access to all the data in the study and had final responsibility for the decision to submit for publication.