From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

作者：Qiqi He, Li Li, Li Dai, Tao Peng, Xiangying Zhang, Yincheng Cai, Xujun Zhang, Renzhong Tang · 发表于：Chinese Journal of Mechanical Engineering · 年份：2024 · DOI：10.1186/s10033-024-00998-7 · 被引用次数：91 · 研究领域：Ergonomics and Human Factors、Digital Transformation in Industry、Technology Assessment and Management

Abstract The human digital twin (HDT) emerges as a promising human-centric technology in Industry 5.0, but challenges remain in human modeling and simulation. Digital human modeling (DHM) provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis. However, it has limitations in real-time data usage, personalized services, and timely interaction. The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment. Hence, this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective. The framework comprises the physical twin, the virtual twin, and the linkage between these two. The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation. HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis, timely feedback, and bidirectional interactions. Finally, the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed. In general, this study outlines a human factors perspective on HDT for the first time, which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.