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As industries evolve with advancement of Industry 4.0 technologies and recently Industry 5.0, incorporating workers’ safety becomes a highlighted need and a challenge for industries. Traditional safety management tools often lack personalization, which limits their effectiveness in current complex industrial setups. This paper reviews existing literature on Human Digital Twin (HDT) framework to identify existing practices, applicable approaches, challenges, and key research gaps. This critical analysis allows identifying opportunities and accordingly presented a conceptual model for HDT that is tailored for physical hazard management in industrial settings. The findings suggest that the HDTs as virtual representations of individual operators could leverage wearable technologies, AI, and existing digital twin (DT) technology to continuously collect, analyze, and predict safety risks based on personal physiological conditions, the individual factors, as well as environmental factors. By applying real-time hazard detection with personalized safety responses, HDTs would enable proactive risk mitigation and improve decision-making in high-risk workplaces. This work contributes to the growing field of research on industry 5.0 by demonstrating the potential of HDTs to improve occupational safety and offering a pathway toward shifting from Industry 4.0 to 5.0.

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A Human Digital Twin Approach to Physical Hazard Management in the Manufacturing Environment: An Extensive Literature Review with a Conceptual Framework

  • 会议论文

  • 发表日期：

    2026年

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  • Rojanat Vongchaisaree,
  • Shiva Abdoli

摘要

As industries evolve with advancement of Industry 4.0 technologies and recently Industry 5.0, incorporating workers’ safety becomes a highlighted need and a challenge for industries. Traditional safety management tools often lack personalization, which limits their effectiveness in current complex industrial setups. This paper reviews existing literature on Human Digital Twin (HDT) framework to identify existing practices, applicable approaches, challenges, and key research gaps. This critical analysis allows identifying opportunities and accordingly presented a conceptual model for HDT that is tailored for physical hazard management in industrial settings. The findings suggest that the HDTs as virtual representations of individual operators could leverage wearable technologies, AI, and existing digital twin (DT) technology to continuously collect, analyze, and predict safety risks based on personal physiological conditions, the individual factors, as well as environmental factors. By applying real-time hazard detection with personalized safety responses, HDTs would enable proactive risk mitigation and improve decision-making in high-risk workplaces. This work contributes to the growing field of research on industry 5.0 by demonstrating the potential of HDTs to improve occupational safety and offering a pathway toward shifting from Industry 4.0 to 5.0.