发表机构
Institute of Systems and Robotics, University of Coimbra; Centre for Mechanical Engineering, Material and Processes, University of Coimbra（科英布拉大学系统机器人研究所; 科英布拉大学机械、材料与工艺中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述工业5.0中工人状态监测、运营管理集成和人机回环架构，指出三者集成缺失是核心挑战，并提出五方向研究议程。
AI 中文摘要
工业5.0（I5.0）将制造业重新定位为以人为中心的愿景，其中信息物理系统必须适应工人，而非反之。尽管在工人状态监测技术方面取得了显著进展，包括无标记计算机视觉、可穿戴生理传感器以及基于机器学习的疲劳和认知负荷估计，但一个关键的集成缺口仍然存在：这些监测能力很少为实时操作决策提供信息，且工人几乎不参与影响其任务和工作量的自适应过程。本文对三个相互关联维度的最新技术进行了结构化综述：（1）工业环境中的工人状态监测，涵盖身体疲劳、认知负荷和人体工程学风险评估；（2）运营管理（OM）模型中的人因集成，考察调度、任务分配和生产规划如何纳入与工人相关的目标；（3）人机回环（HITL）架构，分析工人反馈在多大程度上闭合自适应决策回路。综述揭示，尽管每个维度已独立成熟，但它们之间的集成在很大程度上仍然缺失。基于所审阅的文献，目前尚无公开记录并验证的系统能够将基于实时传感器的工人状态估计与自适应运营管理决策及工人反馈统一于一个闭环架构中。这一缺口被确定为实现工业5.0愿景的核心挑战，并据此提出了一个包含五个方向的研究议程。
英文摘要
Industry 5.0 (I5.0) repositions manufacturing around a human-centric vision in which cyber-physical systems must adapt to the worker rather than the other way around. Despite significant advances in worker state monitoring technologies, including markerless computer vision, wearable physiological sensors, and machine-learning-based fatigue and cognitive load estimation, a critical integration gap persists: these monitoring capabilities rarely inform real-time operational decisions, and workers almost never participate in the adaptive processes that affect their tasks and workloads. This paper presents a structured survey of the state-of-the-art across three interconnected dimensions: (1) worker state monitoring in industrial settings, encompassing physical fatigue, cognitive load, and ergonomic risk assessment; (2) human factors integration in Operations Management (OM) models, examining how scheduling, task allocation, and production planning incorporate worker-related objectives; and (3) Human-in-the-Loop (HITL) architectures, analyzing the extent to which worker feedback closes the adaptive decision loop. The survey reveals that while each dimension has matured independently, the integration across them remains largely absent. Based on the reviewed literature, no publicly documented and validated system connects real-time sensor-driven worker state estimation with adaptive OM decisions and worker feedback in a unified closed-loop architecture. This gap is identified as the central challenge for realizing the I5.0 vision, and a five-direction research agenda is proposed.
CommentsAccepted at the 17th APCA International Conference on Automatic Control and Soft Computing (CONTROLO 2026)
