Applied Sciences · 2023 · 38 citations · 57 references
EngineeringComputer ScienceMechatronics EngineeringAutomationErgonomicsOccupational HealthDigital Twin DesignIndustrial AutomationOccupational SafetySafety ErgonomicsSafety In Human-robot InteractionWorkplace SafetyDigital Twin ApplicationsDigital Twin ArchitectureDigital Twin System DesignDigital Twin-based AssessmentDigital TwinsHuman RepresentationPhysical Asset ModelingReal-world Application
Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions.
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Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
Baotong Chen, Jiafu Wan, Lei Shu et al. · IEEE Access · 2017 · 1.1K citations · Full text