Evidence map›Paper›PMID 40438060›Full record

ArticleFrontiers in public health2025

IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective.

Dhruv Parikh, Avaneesh Karthikeyan, V Ravi, Merin Shibu, Riya Singh, Reka S Sofana

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dhruv ParikhSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Avaneesh KarthikeyanSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
V RaviSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Merin ShibuSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Riya SinghSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Reka S SofanaCentre for Neuroinformatics, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

COVID-19Internet of ThingsMachine LearningHumansMasksPandemicsSARS-CoV-2aarogya setu databasecoronavirus diseaseface mask detectionface shield detectioninternet of thingsmachine learningTkinter GUI

Identifiers

PMID40438060
PMCPMC12116513

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.