Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

Version 1 : Received: 31 December 2020 / Approved: 5 January 2021 / Online: 5 January 2021 (13:32:39 CET)

How to cite: Kaiser, M.S.; Mahmud, M.; Noor, M.B.T.; Zenia, N.Z.; Al Mamun, S.; Mahmud, K.A.; Azad, S.; Aradhya, V.M.; Punitha, S.; Stephan, T.; Kannan, R.; Hanif, M.; Sharmeen, T.; Chen, T.; Hussain, A. iWorkSafe: Towards Healthy Workplaces during COVID-19 with an Intelligent pHealth App for Industrial Settings. Preprints 2021, 2021010092 (doi: 10.20944/preprints202101.0092.v1). Kaiser, M.S.; Mahmud, M.; Noor, M.B.T.; Zenia, N.Z.; Al Mamun, S.; Mahmud, K.A.; Azad, S.; Aradhya, V.M.; Punitha, S.; Stephan, T.; Kannan, R.; Hanif, M.; Sharmeen, T.; Chen, T.; Hussain, A. iWorkSafe: Towards Healthy Workplaces during COVID-19 with an Intelligent pHealth App for Industrial Settings. Preprints 2021, 2021010092 (doi: 10.20944/preprints202101.0092.v1).

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.

Subject Areas

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

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