iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth app for industrial settings

Kaiser, MS, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, Noor, MBT, Zenia, NZ, Mamun, SA, Abir Mahmud, KM, Azad, S, Manjunath Aradhya, VN, Punitha, S, Stephan, T, Kannan, R, Hanif, M, Sharmeen, T, Chen, T ORCID: https://orcid.org/0000-0001-5025-5472 and Hussain, A, 2021. iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth app for industrial settings. IEEE Access.

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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 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.

Item Type:	Journal article
Publication Title:	IEEE Access
Creators:	Kaiser, M.S., Mahmud, M., Noor, M.B.T., Zenia, N.Z., Mamun, S.A., Abir Mahmud, K.M., Azad, S., Manjunath Aradhya, V.N., Punitha, S., Stephan, T., Kannan, R., Hanif, M., Sharmeen, T., Chen, T. and Hussain, A.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	2021
Identifiers:	Number Type 10.1109/access.2021.3050193 DOI 1399332 Other
Rights:	This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	15 Jan 2021 14:12
Last Modified:	31 May 2021 15:07
URI:	https://irep.ntu.ac.uk/id/eprint/42042

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