<italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resultin...

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Main Authors: M. Shamim Kaiser, Mufti Mahmud, Manan Binth Taj Noor, Nusrat Zerin Zenia, Shamim Al Mamun, K. M. Abir Mahmud, Saiful Azad, V. N. Manjunath Aradhya, Punitha Stephan, Thompson Stephan, Ramani Kannan, Mohammed Hanif, Tamanna Sharmeen, Tianhua Chen, Amir Hussain
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9317697/
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author M. Shamim Kaiser
Mufti Mahmud
Manan Binth Taj Noor
Nusrat Zerin Zenia
Shamim Al Mamun
K. M. Abir Mahmud
Saiful Azad
V. N. Manjunath Aradhya
Punitha Stephan
Thompson Stephan
Ramani Kannan
Mohammed Hanif
Tamanna Sharmeen
Tianhua Chen
Amir Hussain
spellingShingle M. Shamim Kaiser
Mufti Mahmud
Manan Binth Taj Noor
Nusrat Zerin Zenia
Shamim Al Mamun
K. M. Abir Mahmud
Saiful Azad
V. N. Manjunath Aradhya
Punitha Stephan
Thompson Stephan
Ramani Kannan
Mohammed Hanif
Tamanna Sharmeen
Tianhua Chen
Amir Hussain
<italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
IEEE Access
Industry 4.0
artificial intelligence
machine learning
mobile app
digital health
safe workplace
author_facet M. Shamim Kaiser
Mufti Mahmud
Manan Binth Taj Noor
Nusrat Zerin Zenia
Shamim Al Mamun
K. M. Abir Mahmud
Saiful Azad
V. N. Manjunath Aradhya
Punitha Stephan
Thompson Stephan
Ramani Kannan
Mohammed Hanif
Tamanna Sharmeen
Tianhua Chen
Amir Hussain
author_sort M. Shamim Kaiser
title <italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
title_short <italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
title_full <italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
title_fullStr <italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
title_full_unstemmed <italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings
title_sort <italic>i</italic>worksafe: towards healthy workplaces during covid-19 with an intelligent phealth app for industrial settings
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.
topic Industry 4.0
artificial intelligence
machine learning
mobile app
digital health
safe workplace
url https://ieeexplore.ieee.org/document/9317697/
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spelling doaj-bcf7d01e2f554687a24a7a5a56ecd0c22021-03-30T15:11:33ZengIEEEIEEE Access2169-35362021-01-019138141382810.1109/ACCESS.2021.30501939317697<italic>i</italic>Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial SettingsM. Shamim Kaiser0https://orcid.org/0000-0002-4604-5461Mufti Mahmud1https://orcid.org/0000-0002-2037-8348Manan Binth Taj Noor2https://orcid.org/0000-0003-3788-8456Nusrat Zerin Zenia3https://orcid.org/0000-0003-0830-1142Shamim Al Mamun4https://orcid.org/0000-0002-2017-7087K. M. Abir Mahmud5https://orcid.org/0000-0002-5913-8448Saiful Azad6https://orcid.org/0000-0002-6234-7267V. N. Manjunath Aradhya7https://orcid.org/0000-0003-0680-1338Punitha Stephan8https://orcid.org/0000-0002-5827-0789Thompson Stephan9https://orcid.org/0000-0002-6578-6919Ramani Kannan10https://orcid.org/0000-0002-5667-5280Mohammed Hanif11https://orcid.org/0000-0001-7703-7918Tamanna Sharmeen12https://orcid.org/0000-0001-6042-6239Tianhua Chen13https://orcid.org/0000-0003-4495-1871Amir Hussain14https://orcid.org/0000-0002-8080-082XInstitute of Information Technology, Jahangirnagar University, Dhaka, BangladeshDepartment of Computer Science, Nottingham Trent University, Nottingham, U.K.Institute of Information Technology, Jahangirnagar University, Dhaka, BangladeshInstitute of Information Technology, Jahangirnagar University, Dhaka, BangladeshInstitute of Information Technology, Jahangirnagar University, Dhaka, BangladeshSkoder Technologies, Dhaka, BangladeshFaculty of Computing, University Malaysia Pahang, Kuantan, MalaysiaDepartment of Computer Applications, JSS Science and Technology University, Mysuru, IndiaDepartment of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, IndiaDepartment of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore, IndiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaDhaka Shishu Hospital, Bangladesh Institute of Child Health, Dhaka, BangladeshWomen Immigrant in Economic Growth, Nottingham, U.K.Department of Computer Science, University of Huddersfield, Huddersfield, U.K.School of Computing, Edinburgh Napier University, Edinburgh, U.K.The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.https://ieeexplore.ieee.org/document/9317697/Industry 4.0artificial intelligencemachine learningmobile appdigital healthsafe workplace