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.
Highlights
The novel Coronavirus Disease 2019 (COVID-19) is caused by a newly found positive-sense single-stranded ribonucleic acid (+ssRNA) virus pathogen which is known as Severe Acute Respiratory Syndrome CoronaVirus–2 (SARS-CoV-2) and matches closely to bat coronaviruses [1], [2]
An intelligent mobile app, called iWorkSafe, has been proposed in this work which checks for the risk of COVID-19 infection among employees to ensure a healthy workplace
The risk estimation process is done through a number of steps which include– 1) analysing the acquired data using different machine learning approaches, 2) fusing the generated knowledge using fuzzy neural network approach, and 3) estimate the risk score which denote the health status of an employee
Summary
The novel Coronavirus Disease 2019 (COVID-19) is caused by a newly found positive-sense single-stranded ribonucleic acid (+ssRNA) virus pathogen which is known as Severe Acute Respiratory Syndrome CoronaVirus–2 (SARS-CoV-2) and matches closely to bat coronaviruses [1], [2]. The proposed mobile app based solution uses an intelligent portable health (pHealth) provision for industrial settings This solution supports instantaneous identification of possible COVID-19 infection using self-test and regular health checkup data, and provides appropriate alert for meticulous tracking based on proximity detection of the workers.
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