Generative AI-Driven Ergonomics: A Virtual-Real Hybrid Experiment for Human Factors Engineering
提出用生成式人工智能增强人因工程研究，通过虚实混合实验补充多样本，提升认知模型的通用性和鲁棒性，案例验证了在人机协同驾驶和航天机械臂操作中的有效性。
Ergonomics or human factors engineering (HFE) mainly exploits human experiments to discover one's cognitive and behavioral mechanisms. Such a paradigm, however, suffers from the scale of subject group and the extent to which they can stand for the whole studied population. Additionally, for real-time human-machine tasks, the experiment-modeling-validation-application path may not be applicable since the experiment cannot be flexibly conducted to update cognitive models, leading to a failure of the online system control and management. To solve the dilemma, this article proposes the generative artificial intelligence (GAI)-driven ergonomics to augment the HFE research. By introducing GAI techniques, virtual-real hybrid experiments are combined and supplement more heterogeneous samples, enhancing the input diversity for cognitive modeling and behavioral learning. The case studies of human-machine cooperative driving and aerospace robotic arm operation indicate that the innovative paradigm can effectively and efficiently augment the human experiment data. It can elevate the generality and robustness of human models.