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Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking
IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of COVID-19 pandemic, IoT`s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763441/ http://dx.doi.org/10.1007/s00607-021-01039-0 |
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author | Tripathy, Hrudaya Kumar Mishra, Sushruta Suman, Shubham Nayyar, Anand Sahoo, Kshira Sagar |
author_facet | Tripathy, Hrudaya Kumar Mishra, Sushruta Suman, Shubham Nayyar, Anand Sahoo, Kshira Sagar |
author_sort | Tripathy, Hrudaya Kumar |
collection | PubMed |
description | IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of COVID-19 pandemic, IoT`s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent symptoms like fever and coughing along with ensuring social distancing norms are properly followed. It comprises three modules which include Cough Detect Module (CDM) for dry cough detection, Temperature Detect module (TDM) for high-temperature monitoring, and Distance Compute Module (DCM) to track social distancing norm violator. The device comprises a combination of a lightweight fabric suspender worn around shoulders and a flexible belt wrapped around the waist. The suspender is equipped with a passive infrared (PIR) sensor and temperature sensor to monitor persistent coughing patterns and high body temperature and the ultra-sonic sensor verify 6 feet distance for tracking an individual's social distancing norms. The developed model is implemented in an aluminum factory to verify its effectiveness. Results obtained were promising and reliable when compared to conventional manual procedures. The model accurately reported when body temperature rises. It outperformed thermal gun as it accurately recorded a mean of only 4.65 candidates with higher body temperature as compared to 8.59% with the thermal gun. A significant reduction of 3.61% on social distance violators was observed. Besides this, the latency delay of 10.32 s was manageable with the participant count of over 800 which makes it scalable. |
format | Online Article Text |
id | pubmed-8763441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-87634412022-01-18 Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking Tripathy, Hrudaya Kumar Mishra, Sushruta Suman, Shubham Nayyar, Anand Sahoo, Kshira Sagar Computing Regular Paper IoT technology is revolutionizing healthcare and is transforming it into more personalized healthcare. In the context of COVID-19 pandemic, IoT`s intervention can help to detect its spread. This research proposes an effective “Smart COVID-Shield” that is capable of automatically detecting prevalent symptoms like fever and coughing along with ensuring social distancing norms are properly followed. It comprises three modules which include Cough Detect Module (CDM) for dry cough detection, Temperature Detect module (TDM) for high-temperature monitoring, and Distance Compute Module (DCM) to track social distancing norm violator. The device comprises a combination of a lightweight fabric suspender worn around shoulders and a flexible belt wrapped around the waist. The suspender is equipped with a passive infrared (PIR) sensor and temperature sensor to monitor persistent coughing patterns and high body temperature and the ultra-sonic sensor verify 6 feet distance for tracking an individual's social distancing norms. The developed model is implemented in an aluminum factory to verify its effectiveness. Results obtained were promising and reliable when compared to conventional manual procedures. The model accurately reported when body temperature rises. It outperformed thermal gun as it accurately recorded a mean of only 4.65 candidates with higher body temperature as compared to 8.59% with the thermal gun. A significant reduction of 3.61% on social distance violators was observed. Besides this, the latency delay of 10.32 s was manageable with the participant count of over 800 which makes it scalable. Springer Vienna 2022-01-18 2022 /pmc/articles/PMC8763441/ http://dx.doi.org/10.1007/s00607-021-01039-0 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Regular Paper Tripathy, Hrudaya Kumar Mishra, Sushruta Suman, Shubham Nayyar, Anand Sahoo, Kshira Sagar Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title | Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title_full | Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title_fullStr | Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title_full_unstemmed | Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title_short | Smart COVID-shield: an IoT driven reliable and automated prototype model for COVID-19 symptoms tracking |
title_sort | smart covid-shield: an iot driven reliable and automated prototype model for covid-19 symptoms tracking |
topic | Regular Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763441/ http://dx.doi.org/10.1007/s00607-021-01039-0 |
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