Abstract
Industry 4.0 has shaped the way people look at the world and interact with it, especially concerning the work environment with respect to occupational safety and health (OSH). Machine learning (ML), as a branch of Artificial Intelligence (AI), can be effectively used to create expert systems to exhibit intelligent behavior to provide solutions to complicated problems and finally process massive data. Therefore, a study is proposed to provide the best methodological practice in the light of ML. Alongside the review of previous investigations, the following research aims to determine the ML approaches appropriate to OSH issues. In other words, highlighting specific ML methodologies, which have been employed successfully in others areas. Bearing this objective in mind, one can identify an appropriate ML technique to solve a problem in the OSH domain. Accordingly, several questions were designed to conduct the research. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Protocols and Systematic Reviews were used to draw the research outline. The chosen databases were SCOPUS, PubMed, Science Direct, Inspect, and Web of Science. A set of keywords related to the topic were defined, and both exclusion and inclusion criteria were determined. All of the eligible papers will be analyzed, and the extracted information will be included in an Excel form sheet. The results will be presented in a narrative-based form. Additionally, all tables summarizing the most important findings will be offered.
Highlights
1.1 BackgroundThroughout the years, occupational health and safety practitioners, researchers, and managers have addressed the physical, chemical, biological, ergonomic, and organizational environment issues using traditional methods
Industry 4.0 has shaped the way people look at the world and interact with it, especially concerning the work environment with respect to occupational safety and health (OSH)
Alongside the review of previous investigations, the following research aims to determine the Machine learning (ML) approaches appropriate to OSH issues
Summary
Throughout the years, occupational health and safety practitioners, researchers, and managers have addressed the physical, chemical, biological, ergonomic, and organizational environment issues using traditional methods. ML methods have been used for safety training (Cho et al, 2018), personal protective equipment improvement (Ellena et al, 2016), environmental health surveillance (Xie and Chang, 2019), wellness and health promotion in different occupations like construction, petroleum, firefighting, office workers, drivers, among others (Jin et al, 2019). It could improve occupational safety and health (OSH) measures and equipment from one side, and influence the quality of new methods, used to measure, assess, and control hazards from the other side (Moore, 2019). The systematic review aims to answer a list of relevant topics offering a detailed analysis regarding the innovative and automated methods in this field, enabling a clearer view of the stakeholders generating and utilizing ML environments
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