Received January 1, 2021, accepted January 5, 2021, date of publication January 8, 2021, date of current version January 26, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3050193 i Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings M. SHAMIM KAISER 1 , (Senior Member, IEEE), MUFTI MAHMUD 2,3 , (Senior Member, IEEE), MANAN BINTH TAJ NOOR 1 , NUSRAT ZERIN ZENIA 1 , SHAMIM AL MAMUN 1 , (Member, IEEE), K. M. ABIR MAHMUD 4 , SAIFUL AZAD 5 , V. N. MANJUNATH ARADHYA 6 , PUNITHA STEPHAN 7 , THOMPSON STEPHAN 8 , (Member, IEEE), RAMANI KANNAN 9 , (Senior Member, IEEE), MOHAMMED HANIF 10 , TAMANNA SHARMEEN 11 , TIANHUA CHEN 12 , (Member, IEEE), AND AMIR HUSSAIN 13 , (Senior Member, IEEE) 1 Institute of Information Technology, Jahangirnagar University, Dhaka 1342, Bangladesh 2 Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, U.K. 3 Medical Technology Innovation Facility, Nottingham Trent University, Nottingham NG11 8NS, U.K. 4 Skoder Technologies, Dhaka 1216, Bangladesh 5 Faculty of Computing, University Malaysia Pahang, Kuantan 26300, Malaysia 6 Department of Computer Applications, JSS Science and Technology University, Mysuru 570006, India 7 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India 8 Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore 560054, India 9 Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32160, Malaysia 10 Bangladesh Institute of Child Health, Dhaka Shishu Hospital, Dhaka 1207, Bangladesh 11 Women Immigrant in Economic Growth, Nottingham NG2 7JZ, U.K. 12 Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, U.K. 13 School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, U.K. Corresponding authors: M. Shamim Kaiser (mskaiser@juniv.edu) and Mufti Mahmud (muftimahmud@gmail.com) This work was partially supported by Nottingham Trent University, U.K. 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 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. INDEX TERMS Industry 4.0, artificial intelligence, machine learning, mobile app, digital health, safe workplace, worker safety, Coronavirus. The associate editor coordinating the review of this manuscript and approving it for publication was Derek Abbott . 13814 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 9, 2021