Kaiser, M. Shamim and Mahmud, Mufti and Taj Noor, Manan Binth and Zenia, Nusrat Zerin and Al Mamun, Shamim and Abir Mahmud, K. M. and Azad, Saiful and Manjunath Aradhya, V. N. and Stephan, Punitha and Stephan, Thompson and Kannan, Ramani and Hanif, Mohammed and Sharmeen, Tamanna and Chen, Tianhua and Hussain, Amir (2021) iWorksafe: Towards healthy workplaces during COVID-19 with an intelligent phealth app for industrial settings. IEEE Access, 9 (9317697). 13814 -13828. ISSN 2169-3536. (Published)
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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 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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial intelligence; Coronavirus; Digital health; Industry 4.0; Machine learning; Mobile app; Safe workplace; Worker safety |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 20 Aug 2021 08:36 |
| Last Modified: | 20 Aug 2021 08:36 |
| URI: | http://umpir.ump.edu.my/id/eprint/31832 |
| Download Statistic: | View Download Statistics |
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