Abstract
Airborne diseases are easy to spread in any population. The advent of COVID-19 showed us that we are not prepared to control them. The pandemic has drastically posed challenges to the daily functioning of public and private establishments. In general, while there have been several approaches to reduce the potential risk of spreading the virus, many of them rely on the commitment that people make, which - unfortunately - cannot be constant, for example, wearing a facemask in closed environment at all times or social distancing. In this work, we propose a computer vision system to determine the risk of airborne disease spread in closed environments. We modify and implement the Wells-Riley epidemiological equation. We also evaluate and implement models for facemask and person detection from OpenVino. For mask detection, we applied transfer learning and obtained the best performance for a model based on MobileNetV2. The generated data from several devices is visible in a web platform to monitor multiple areas and locations. Finally, an OAK-D camera and a Jetson device are embedded in a end device meant to monitor a closed environment and send spread risk data continually to the web platform. Our results are promising as we obtained up to 88% of accuracy for the person detection task and up to 57% of mAP for the facemask task. We expect this paper to be beneficial for developing new control measurements and prevention tools to prevent airborne contagion.
This research was supported by the OpenCV Foundation through an award provided at the OpenCV AI Competition 2021.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Park, S.E.: Epidemiology, virology, and clinical features of severe acute respiratory syndrome -coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19). Clin. Exp. Pediatr. 63(4), 119–124 (2020). https://pubmed.ncbi.nlm.nih.gov/32252141
WHO: Coronavirus (COVID-19) Statistics. https://covid19.who.int/
Himeur, Y., et al.: Deep visual social distancing monitoring to combat COVID-19: a comprehensive survey. Sustain. Cities Soc. 85, 104064 (2022). https://www.sciencedirect.com/science/article/pii/S2210670722003821
Ahmed, I., Ahmad, M., Rodrigues, J.J.P.C., Jeon, G., Din, S.: A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. 65, 102571 (2021). https://www.sciencedirect.com/science/article/pii/S2210670720307897
Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B., Alikhani, E.: An automatic system to monitor the physical distance and face mask wearing of construction workers in COVID-19 pandemic. CoRR, vol. abs/2101.0 (2021). https://arxiv.org/abs/2101.01373
Eyiokur, F.I., Ekenel, H.K., Waibel, A.: A computer vision system to help prevent the transmission of COVID-19. Undefined (2021)
Petrovic, N., Kocić, D.: IoT-based system for COVID-19 indoor safety monitoring (2020)
Degadwala, S., Vyas, D., Dave, H., Mahajan, A.: Visual social distance alert system using computer vision deep learning. In: Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020, pp. 1512–1516 (2020)
Yang, D., Yurtsever, E., Renganathan, V., Redmill, K.A., Özgüner, Ü.: A vision-based social distancing and critical density detection system for COVID-19. Sensors 21(13), 4608 (2021). https://www.mdpi.com/1424-8220/21/13/4608/htm
Karaman, O., Alhudhaif, A., Polat, K.: Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19. Appl. Soft Comput. 110, 107610 (2021)
Li, J., Wu, Z.: The application of yolov4 and a new pedestrian clustering algorithm to implement social distance monitoring during the COVID-19 pandemic. J. Phys.: Conf. Ser. 1865(4) (2021). https://doi.org/10.1088/1742-6596/1865/4/042019
Rezaei, M., Azarmi, M.: DeepSOCIAL: social distancing monitoring and infection risk assessment in COVID-19 pandemic. Appl. Sci. 10(21) (2020). https://www.mdpi.com/2076-3417/10/21/7514
Kamalasanan, V., Sester, M.: Living with rules: an AR approach. In: Adjunct Proceedings of the 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2020, pp. 213–216 (2020)
Delta variant: 8 things you should know \(|\) Coronavirus \(|\) UC Davis Health. https://health.ucdavis.edu/coronavirus/covid-19-information/delta-variant.html
COVID-19 vaccine tracker: View vaccinations by country. https://edition.cnn.com/interactive/2021/health/global-covid-vaccinations/
Li, M., Varble, N., Turkbey, B., Xu, S., Wood, B.J.: Camera-based distance detection and contact tracing to monitor potential spread of COVID-19. In: Mello-Thoms, C.R., Taylor-Phillips, S. (eds.) Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, vol. 12035, p. 120351D. International Society for Optics and Photonics. SPIE (2022). https://doi.org/10.1117/12.2612846
ibaiGorordo: Ibaigorordo/social-distance-feedback-for-the-blind: a social distancing feedback system for the blind using the oak-d camera. https://github.com/ibaiGorordo/Social-Distance-Feedback-For-The-Blind
Kanjee, R.: Social distance detection system - using raspberry pi and OpenCV AI kit. https://medium.com/augmented-startups/social-distance-detection-system-using-raspberry-pi-and-opencv-ai-kit-97fd68ff8dd4
Dai, Z., Jiang, Y., Li, Y., Liu, B., Chan, A.B., Vasconcelos, N.: BEV-net: assessing social distancing compliance by joint people localization and geometric reasoning. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5381–5391 (2021)
Mittal, R., Meneveau, C., Wu, W.: A mathematical framework for estimating risk of airborne transmission of COVID-19 with application to face mask use and social distancing. Phys. Fluids 32, 101903 (2020)
Wells, W.: Airborne Contagion and Air Hygiene. An Ecological Study of Droplet Infections. Cambridge, MA (1955)
Riley, E., Murphy, G., Riley, R.: Airborne spread of measles in a suburban elementary school. Am. J. Epidemiol. 107, 421–432 (1978)
To, G.N.S., Chao, C.Y.H.: Review and comparison between the wells-riley and dose-response approaches to risk assessment of infectious respiratory diseases. Indoor Air 20, 2 (2010)
Mikszewski, A., Stabile, L., Buonanno, G., Morawska, L.: The airborne contagiousness of respiratory viruses: a comparative analysis and implications for mitigation. Geosci. Front. 13, 101285 (2021)
Li, J., et al.: Evaluation of infection risk for SARS-CoV-2 transmission on university campuses. Sci. Technol. Built Environ. 27, 1165–1180 (2021)
Buonanno, G., Morawska, L., Stabile, L.: Quantitative assessment of the risk of airborne transmission of SARS-CoV-2 infection: prospective and retrospective applications. Environ. Int. 145, 106112 (2020)
Adams, W.: Measurement of breathing rate and volume in routinely performed daily activities (1993)
Teppner, R., Langensteiner, B., Meile, W., Brenn, G., Kerschbaumer, S.: Air change rates driven by the flow around and through a building storey with fully open or tilted windows: an experimental and numerical study. Energy Build. 80, 570–583 (2014)
Eikenberry, S.E., et al.: To mask or not to mask: modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect. Dis. Modell. 5 (2020)
Computer Vision Annotation Tool (2022). https://cvat.org/
GotG: How to train an object detector using mobilenet SSD V2 (2020). https://github.com/GotG/test_object_detection_demo/tree/master/data/medmask_voc
Roboflow (2022). https://roboflow.com/
Overview of OpenVINO™ Toolkit Intel’s Pre-trained Models (2022). https://docs.openvino.ai/latest/omz_models_group_intel.html
Luxonis: Luxonis mytiadx blob converter. http://blobconverter.luxonis.com
Luxonis: Spatial location calculator. https://docs.luxonis.com/projects/api/en/latest/components/nodes/spatial_location_calculator/
Velarde, F., Rub, R., Mamani-Paco, R., Andrade-Flores, M.: Estimation of the probability of contagion of COVID-19 by aerosols in closed environments: applications to cases in the city of La Paz, Bolivia, vol. 37, pp. 22–30 (2020) http://www.scielo.org.bo/scielo.php?script=sci_arttext &pid=S1562-38232020000200004 &lng=es &nrm=iso
Liu, Z., et al.: Potential infection risk assessment of improper bioaerosol experiment operation in one BSL-3 laboratory based on the improved wells-riley method. Build. Environ. 201, 107974 (2021)
Guo, Y., et al.: Assessing and controlling infection risk with wells-riley model and spatial flow impact factor (SFIF). Sustain. Cities Soc. 67, 102719 (2021)
Wang, Z., Galea, E.R., Grandison, A., Ewer, J., Jia, F.: A coupled computational fluid dynamics and wells-riley model to predict COVID-19 infection probability for passengers on long-distance trains. Saf. Sci. 147, 105572 (2022)
GotG: How to train an object detector using mobilenet SSD V2 (2020). https://github.com/GotG/test_object_detection_demo
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rojas, W., Salcedo, E., Sahonero, G. (2023). ADRAS: Airborne Disease Risk Assessment System for Closed Environments. In: Lossio-Ventura, J.A., Valverde-Rebaza, J., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2022. Communications in Computer and Information Science, vol 1837. Springer, Cham. https://doi.org/10.1007/978-3-031-35445-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-35445-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35444-1
Online ISBN: 978-3-031-35445-8
eBook Packages: Computer ScienceComputer Science (R0)