Kaiser, M.S., Mahmud, M. ORCID: 0000-0002-2037-8348, Noor, M.B.T., Zenia, N.Z., Mamun, S.A., Abir Mahmud, K.M., Azad, S., Manjunath Aradhya, V.N., Punitha, S., Stephan, T., Kannan, R., Hanif, M., Sharmeen, T., Chen, T. ORCID: 0000-0001-5025-5472 and Hussain, A., 2021. iWorkSafe: towards healthy workplaces during COVID-19 with an intelligent pHealth app for industrial settings. IEEE Access.
|
Text
1399332_Mahmud.pdf - Post-print Download (4MB) | Preview |
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 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: | Journal article | ||||||
---|---|---|---|---|---|---|---|
Publication Title: | IEEE Access | ||||||
Creators: | Kaiser, M.S., Mahmud, M., Noor, M.B.T., Zenia, N.Z., Mamun, S.A., Abir Mahmud, K.M., Azad, S., Manjunath Aradhya, V.N., Punitha, S., Stephan, T., Kannan, R., Hanif, M., Sharmeen, T., Chen, T. and Hussain, A. | ||||||
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
Date: | 2021 | ||||||
Identifiers: |
|
||||||
Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. | ||||||
Divisions: | Schools > School of Science and Technology | ||||||
Record created by: | Linda Sullivan | ||||||
Date Added: | 15 Jan 2021 14:12 | ||||||
Last Modified: | 31 May 2021 15:07 | ||||||
URI: | https://irep.ntu.ac.uk/id/eprint/42042 |
Actions (login required)
Edit View |
Views
Views per month over past year
Downloads
Downloads per month over past year