i Worksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

• Published in: IEEE access 2021, Vol.9, p.13814-13828
• Main Authors: Kaiser, M. Shamim, Mahmud, Mufti, Noor, Manan Binth Taj, Zenia, Nusrat Zerin, Mamun, Shamim Al, Mahmud, K. M. Abir, Azad, Saiful, Aradhya, V. N. Manjunath, Stephan, Punitha, Stephan, Thompson, Kannan, Ramani, Hanif, Mohammed, Sharmeen, Tamanna, Chen, Tianhua, Hussain, Amir
• Format: Article
• Language: English
• Subjects: Applications programs; Artificial neural networks; Bluetooth; Coronaviruses; COVID-19; Data collection; Decision trees; Disease control; Disease transmission; Electronic devices; Employees; Fuzzy logic; Health care; Mobile computing; Neural networks; Personal health; Regression models; Self tests; Viral diseases; Workplaces
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3050193

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 [Formula Omitted]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 [Formula Omitted]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.