%0 Journal Article %@ 21693536 %A Shamim Kaiser, M. %A Mahmud, M. %A Noor, M.B.T. %A Zenia, N.Z. %A Mamun, S.A. %A Abir Mahmud, K.M. %A Azad, S. %A Manjunath Aradhya, V.N. %A Stephan, P. %A Stephan, T. %A Kannan, R. %A Hanif, M. %A Sharmeen, T. %A Chen, T. %A Hussain, A. %D 2021 %F scholars:15942 %I Institute of Electrical and Electronics Engineers Inc. %J IEEE Access %K 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 %P 13814-13828 %R 10.1109/ACCESS.2021.3050193 %T IWorksafe: Towards Healthy Workplaces during COVID-19 with an Intelligent Phealth App for Industrial Settings %U https://khub.utp.edu.my/scholars/15942/ %V 9 %X 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. %Z cited By 64