%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