Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

iWorkSafe: Towards Healthy Workplaces during COVID-19 with an Intelligent pHealth App for Industrial Settings

M. Shamim Kaiser
* ORCID logo ,
Mufti Mahmud
* ORCID logo ,
Manan Binth Taj Noor
,
Nusrat Zerin Zenia
,
Shamim Al Mamun
,
K.M. Abir Mahmud
,
Saiful Azad
,
V.N. Manjunath Aradhya
,
S. Punitha
,
Thompson Stephan
,
Ramani Kannan
ORCID logo ,
Mohammed Hanif
,
Tamanna Sharmeen
,
Tianhua Chen
ORCID logo ,
Amir Hussain
Version 1 : Received: 31 December 2020 / Approved: 5 January 2021 / Online: 5 January 2021 (13:32:39 CET)

A peer-reviewed article of this Preprint also exists.

M. S. Kaiser et al., "iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings," in IEEE Access, vol. 9, pp. 13814-13828, 2021, doi: 10.1109/ACCESS.2021.3050193. M. S. Kaiser et al., "iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings," in IEEE Access, vol. 9, pp. 13814-13828, 2021, doi: 10.1109/ACCESS.2021.3050193.

Abstract

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 iWorkSafe, 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 iWorkSafe 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 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 employee 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.

Keywords

Industry 4.0; artificial intelligence; machine learning; mobile app; digital health; safe workplace; worker safety; Coronavirus

Subject

Computer Science and Mathematics, Software

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.