IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective
COVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure against the spread of the virus that caused the COVID-19 pandemic SARS-CoV-2, is to use face coverings in public settings. In this study, we present a po...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1552515/full |
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| author | Dhruv Parikh Avaneesh Karthikeyan V. Ravi Merin Shibu Riya Singh Reka S. Sofana |
| author_facet | Dhruv Parikh Avaneesh Karthikeyan V. Ravi Merin Shibu Riya Singh Reka S. Sofana |
| author_sort | Dhruv Parikh |
| collection | DOAJ |
| description | COVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure against the spread of the virus that caused the COVID-19 pandemic SARS-CoV-2, is to use face coverings in public settings. In this study, we present a potential application of the Internet of Things (IoT) and machine learning to prevent the spread of COVID-19. The proposed smart gateway entrance system consists of various subsystems: face mask recognition, face shield detection, face mask detection with face shields, sanitization systems, temperature monitoring systems, and vaccine verification. These systems help us to efficiently monitor, authenticate, track health parameters, and process data in real-time. The face mask and face shield detection subsystems leverage a hybrid model that combines the capabilities of MobileNetV2 and VGG19, enabling more robust and accurate detection by leveraging MobileNetV2′s efficiency and VGG19′s depth in feature extraction, which has an overall accuracy of 97% and notably the face shield detection component obtains an efficiency of 99%. Proposed framework includes QR code-based vaccination certificate authentication using a secure real-time database model, inspired by health platforms such as CoWIN, to ensure reliable and timely verification at points of entry and the real-time database management system developed using Haar Cascade trainer GUI helps to integrate all the data in real-time and provides access to the entry. The IoT model sanitizes individuals and tracks health parameters using an MLX90614 infrared sensor with an accuracy of ±0.5°C. As the system updates the real-time database, it helps maintain a record of the employee's health conditions and checks whether the employee follows all safety screening protocols every day. Therefore, the proposed system has immense potential to contribute to community healthcare and fight against COVID-19. |
| format | Article |
| id | doaj-art-4952c38b63e945e2bfa9ee08cb245c89 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-4952c38b63e945e2bfa9ee08cb245c892025-08-20T03:49:33ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15525151552515IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspectiveDhruv Parikh0Avaneesh Karthikeyan1V. Ravi2Merin Shibu3Riya Singh4Reka S. Sofana5School of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaCentre for Neuroinformatics, School of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaCOVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure against the spread of the virus that caused the COVID-19 pandemic SARS-CoV-2, is to use face coverings in public settings. In this study, we present a potential application of the Internet of Things (IoT) and machine learning to prevent the spread of COVID-19. The proposed smart gateway entrance system consists of various subsystems: face mask recognition, face shield detection, face mask detection with face shields, sanitization systems, temperature monitoring systems, and vaccine verification. These systems help us to efficiently monitor, authenticate, track health parameters, and process data in real-time. The face mask and face shield detection subsystems leverage a hybrid model that combines the capabilities of MobileNetV2 and VGG19, enabling more robust and accurate detection by leveraging MobileNetV2′s efficiency and VGG19′s depth in feature extraction, which has an overall accuracy of 97% and notably the face shield detection component obtains an efficiency of 99%. Proposed framework includes QR code-based vaccination certificate authentication using a secure real-time database model, inspired by health platforms such as CoWIN, to ensure reliable and timely verification at points of entry and the real-time database management system developed using Haar Cascade trainer GUI helps to integrate all the data in real-time and provides access to the entry. The IoT model sanitizes individuals and tracks health parameters using an MLX90614 infrared sensor with an accuracy of ±0.5°C. As the system updates the real-time database, it helps maintain a record of the employee's health conditions and checks whether the employee follows all safety screening protocols every day. Therefore, the proposed system has immense potential to contribute to community healthcare and fight against COVID-19.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1552515/fullcoronavirus diseaseface mask detectionface shield detectionTkinter GUIaarogya setu databaseinternet of things |
| spellingShingle | Dhruv Parikh Avaneesh Karthikeyan V. Ravi Merin Shibu Riya Singh Reka S. Sofana IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective Frontiers in Public Health coronavirus disease face mask detection face shield detection Tkinter GUI aarogya setu database internet of things |
| title | IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective |
| title_full | IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective |
| title_fullStr | IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective |
| title_full_unstemmed | IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective |
| title_short | IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective |
| title_sort | iot and ml driven framework for managing infectious disease risks in communal spaces a post covid perspective |
| topic | coronavirus disease face mask detection face shield detection Tkinter GUI aarogya setu database internet of things |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1552515/full |
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