IWorksafe: Towards Healthy Workplaces during COVID-19 with an Intelligent Phealth App for Industrial Settings

Shamim Kaiser, M. and Mahmud, M. and Noor, M.B.T. and Zenia, N.Z. and Mamun, S.A. and Abir Mahmud, K.M. and Azad, S. and Manjunath Aradhya, V.N. and Stephan, P. and Stephan, T. and Kannan, R. and Hanif, M. and Sharmeen, T. and Chen, T. and Hussain, A. (2021) IWorksafe: Towards Healthy Workplaces during COVID-19 with an Intelligent Phealth App for Industrial Settings. IEEE Access, 9 . pp. 13814-13828.

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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. © 2013 IEEE.

Item Type:Article
Impact Factor:cited By 8
Uncontrolled Keywords:Computer viruses; Decision trees; Fuzzy neural networks; Health risks; Locks (fasteners); Logistic regression; mHealth; Nearest neighbor search; Risk perception; Statistical tests; Viruses, Bluetooth low energies (BTLE); Contact-tracing data; Fuzzy neural network model; High transmission rate; Industrial settings; K-nearest neighbors; Logistic Regression modeling; Sensing technology, Personnel
ID Code:23963
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:23
Last Modified:19 Aug 2021 13:23

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