<em>i</em>WorkSafe: Towards Healthy Workplaces during COVID-19 with an Intelligent pHealth App for Industrial Settings

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&amp;rsquo; 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 employee 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.

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

“…Artificial intelligence has been used for analysis of patterns and classification in diverse fields such as, anomaly detection [29,[36][37][38][39][40][41][42][43][44], biological data mining [45,46], disease detection [47][48][49][50][51][52][53][54][55][56][57][58], monitoring of human [59][60][61][62], financial forecasting [63], image analysis [64,65], and natural language processing [66][67][68]. Most of the time, these algorithms are composed of multiple layers of neurons for processing of non-linear information and were inspired by how the human brain works.…”