Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Aug 12;203:727–732. doi: 10.1016/j.procs.2022.07.108

A Convolutional Neural Network-enabled IoT framework to verify COVID-19 hygiene conditions and authorize access to facilities

Ticiana Capris a, Yuka Takagi a, Diana Figueiredo a, João Henriques a,b,c, Ivan Miguel Pires d,e
PMCID: PMC9374321  PMID: 35974969

Abstract

COVID-19 has infected several million of individuals while claiming numerous lives. This fact raised the need to apply the measure to prevent its transmission. The use of disinfection products, wearing masks, and avoiding touching doors are important measures to avoid its spread. Thus, this work proposes a framework supported by a Convolutional Neural Network (CNN) model checking the hygienic conditions of the individuals requiring authorization to access facilities. The experimental work takes IoT devices with sensors to check: whether the users have disinfection product in their hands and a trained model to check whether individuals are also wearing masks. The achieved results highlighted the effectiveness of the proposed framework.

Keywords: IoT, Covid-19; Sensor; Convolutional Neural Network

References

  • 1.Wang Liang, Didelot Xavier, Yang Jing, Wong Gary, Shi Yi, Liu Wenjun, Gao George F, Bi Yuhai. Inference of person-to-person transmission of covid-19 reveals hidden super-spreading events during the early outbreak phase. Nature communications. 2020;11(1):1–6. doi: 10.1038/s41467-020-18836-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Li Junjiang, Giabbanelli Philippe, et al. Returning to a normal life via covid-19 vaccines in the united states: a large-scale agent-based simulation study. JMIR medical informatics. 2021;9(4):e27419. doi: 10.2196/27419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Forstmeier Wolfgang, Wagenmakers Eric-Jan, Parker Timothy H. Detecting and avoiding likely false-positive fndings–a practical guide. Biological Reviews. 2017;92(4):1941–1968. doi: 10.1111/brv.12315. [DOI] [PubMed] [Google Scholar]
  • 4.Costa Marcelo, Rodrigues Margarida, Baptista Pedro, Henriques Joa˜o, Pires Ivan Miguel, Wanzeller Cristina, Caldeira Filipe. International Conference on Smart Objects and Technologies for Social Good. Springer; 2021. Covid-19 next day trend forecast; pp. 44–50. pages. [Google Scholar]
  • 5.Wyk Franco Van, Wang Yiyang, Khojandi Anahita, Masoud Neda. Real-time sensor anomaly detection and identifcation in automated vehicles. IEEE Transactions on Intelligent Transportation Systems. 2019;21(3):1264–1276. [Google Scholar]
  • 6.Capris Ticiana, Melo Pedro, Pereira Pedro, Morgado Jose´, Garcia Nuno M, Pires Ivan Miguel. International Summit Smart City 360°. Springer; 2020. Approach for the development of a system for covid-19 preliminary test; pp. 117–124. pages. [Google Scholar]
  • 7.Seshadri Dhruv R, Davies Evan V, Harlow Ethan R, Hsu Jefrey J, Knighton Shanina C, Walker Timothy A, Voos James E, Drummond Colin K. Wearable sensors for covid-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Frontiers in Digital Health. 2020:8. doi: 10.3389/fdgth.2020.00008. page. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Nahla Khamis Ibrahim. Epidemiologic surveillance for controlling covid-19 pandemic: types, challenges and implications. Journal of infection and public health. 2020;13(11):1630–1638. doi: 10.1016/j.jiph.2020.07.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Nižetić Sandro. Impact of coronavirus (covid-19) pandemic on air transport mobility, energy, and environment: A case study. International Journal of Energy Research. 2020;44(13):10953–10961. [Google Scholar]
  • 10.Honein Margaret A, Christie Athalia, Rose Dale A, Brooks John T, Meaney-Delman Dana, Cohn Amanda, Sauber-Schatz Erin K, Walker Allison, Cliford McDonald L, Liburd Leandris C, et al. Summary of guidance for public health strategies to address high levels of community transmission of sars-cov-2 and related deaths. Morbidity and Mortality Weekly Report. december 2020;69(49):1860, 2020. doi: 10.15585/mmwr.mm6949e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Dunton Genevieve F, Do Bridgette, Wang Shirlene D. Early efects of the covid-19 pandemic on physical activity and sedentary behavior in children living in the us. BMC public health. 2020;20(1):1–13. doi: 10.1186/s12889-020-09429-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Lee In, Lee Kyoochun. The internet of things (iot): Applications, investments, and challenges for enterprises. Business horizons. 2015;58(4):431–440. [Google Scholar]
  • 13.Oztemel Ercan, Gursev Samet. Literature review of industry 4.0 and related technologies. Journal of Intelligent Manufacturing. 2020;31(1):127–182. [Google Scholar]
  • 14.West Darrell M. Vol. 3. Brookings Center for Technology Innovation; 2016. pp. 1–20. (How 5g technology enables the health internet of things). [Google Scholar]
  • 15.Ivan Miguel Pires. 2020 International Conference on Decision Aid Sciences and Application (DASA) IEEE; 2020. A review on diagnosis and treatment methods for coronavirus disease with sensors; pp. 219–223. pages. [Google Scholar]
  • 16.Ivan Miguel Pires. 2021 International Conference on Decision Aid Sciences and Application (DASA) IEEE; 2021. A brief analysis on the use of artifcial neural networks for covid-19 pandemic in portugal; pp. 712–715. pages. [Google Scholar]
  • 17.Hiremath Shivayogi, Yang Geng, Mankodiya Kunal. 2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH) IEEE; 2014. Wearable internet of things: Concept, architectural components and promises for person-centered healthcare; pp. 304–307. pages. [Google Scholar]
  • 18.Felizardo Virginie, Sousa Paula, Sabugueiro Daniel, Alexandre Celina, Couto Rafael, Garcia Nuno, Pires Ivan. Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services. IGI global; 2015. E-health: current status and future trends; pp. 302–326. pages. [Google Scholar]
  • 19.Darwish Ashraf, Hassanien Aboul Ella, Elhoseny Mohamed, Sangaiah Arun Kumar, Muhammad Khan. The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing. 2019;10(10):4151–4166. [Google Scholar]
  • 20.Bamakan Seyed Mojtaba Hosseini, Rahbar Ehsan, Gheisari Mehdi. The role of wearable technology in the diagnosis and prevention of covid-19. Journal of Research and Health. 2021;11(4):213–214. [Google Scholar]
  • 21.Mabrook S Al-Rakhami, Md Milon Islam, Md Zabirul Islam, Amanullah Asraf, Ali Hassan Sodhro, and Weiping Ding. Diagnosis of covid-19 from x-rays using combined cnn-rnn architecture with transfer learning. MedRxiv, pages 2020–08, 2021.
  • 22.Moshayedi Ata Jahangir, Roy Atanu Shuvam, Liao Liefa, Li Shuai. 2019 6th International Conference on Information Science and Control Engineering (ICISCE) IEEE; 2019. Raspberry pi scada zonal based system for agricultural plant monitoring; pp. 427–433. pages. [Google Scholar]
  • 23.Moshayedi Ata Jahangir, Roy Atanu Shuvam, Kolahdooz Amin, Shuxin Yang. Deep learning application pros and cons over algorithm. EAI Endorsed Transactions on AI and Robotics. 2022;1:1–13. [Google Scholar]
  • 24.Alzubaidi Laith, Zhang Jinglan, Humaidi Amjad J, Al-Dujaili Ayad, Duan Ye, Al-Shamma Omran, Santamar´ıa J, Fadhel Mohammed A, Al-Amidie Muthana, Farhan Laith. Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. Journal of big Data. 2021;8(1):1–74. doi: 10.1186/s40537-021-00444-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Gulli Antonio, Kapoor Amita, Pal Sujit. Packt Publishing Ltd; 2019. Deep learning with TensorFlow 2 and Keras: regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. [Google Scholar]
  • 26.Sahu Priyanka, Dixit Sakshi, Mishra Shruti, Srivastava Smriti. Alcohol detection based engine locking system using mq-3 sensor. International Research Journal of Engineering and Technology. 2017;4(4):979–981. [Google Scholar]
  • 27.Moroney Laurence, Moroney Anglin, Anglin . Springer; 2017. Defnitive Guide to Firebase. [Google Scholar]
  • 28.Bean Martin. Packt Publishing Ltd; 2015. Laravel 5 essentials. [Google Scholar]
  • 29.Github - chandrikadeb7/face-mask-detection: Face mask detection system based on computer vision and deep learning using opencv and tensorfow/keras. https://github.com/chandrikadeb7/Face-Mask-Detection, 2022.
  • 30.Adil Shaik and Uma Vidyadhari Chetlur. Design and implementation of an ai-based face recognition model in docker container on iot platform, 2020.
  • 31.Salih Fatma, Omer Mysoon SA. 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) IEEE; 2018. Raspberry pi as a video server; pp. 1–4. pages. [Google Scholar]
  • 32.Voss Henike Guilherme Jordan, Ju´nior Jose´ Jair Alves Mendes, Farinelli Murilo Eduardo, Stevan Sergio Luiz. A prototype to detect the alcohol content of beers based on an electronic nose. Sensors. 2019;19(11):2646. doi: 10.3390/s19112646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Boranbayev Askar, Boranbayev Seilkhan, Nurbekov Askar. Proceedings of SAI Intelligent Systems Conference. Springer; 2020. Java based application development for facial identifcation using opencv library; pp. 77–85. pages. [Google Scholar]

Articles from Procedia Computer Science are provided here courtesy of Elsevier

RESOURCES