Human–Agent Collaborative Ergonomics: Multimodal Measurement, Explainable AI, and Ethical Governance

The rapid integration of intelligent agents—such as collaborative robots, autonomous systems, and AI-driven decision-support tools—into industrial and service workplaces is fundamentally reshaping occupational ergonomics. Unlike traditional automation, modern human–agent collaboration requires continuous physical, cognitive, and perceptual interaction between human operators and adaptive, learning-enabled systems. While these technologies promise gains in productivity, safety, and system reliability, they also introduce new ergonomic risks, including cognitive overload, stress, fatigue and emergent human error modes.Recent advances in multimodal sensing—including physiological signals (e.g., heart rate variability, electrodermal activity, EEG), behavioral measures (e.g., facial features, motion capture, posture, gaze), and contextual system data—enable unprecedented insight into human workload, stress, fatigue, and interaction quality during real-time collaboration with intelligent agents. However, the interpretation of these high-dimensional data streams increasingly relies on complex AI models whose decision logic is often opaque to end users and system designers. This tension highlights the paradox of relying on opaque, black-box AI models to assess fundamentally human-centered outcomes such as comfort, safety, and well-being.Compounding these challenges are practical constraints common in ergonomics research, including small, heterogeneous human datasets, limited generalizability across tasks and populations, and ethical concerns surrounding continuous monitoring of human operators. Addressing these issues requires interdisciplinary advances that integrate ergonomics, human factors, AI, systems engineering, and ethical governance. This Research Topic responds to this need by positioning human-centered ergonomics at the core of intelligent human–agent systems design.This Research Topic aims to advance theory, methods, and practice in occupational ergonomics for human–agent collaboration by integrating multimodal measurement, explainable AI, and ethical frameworks. The objective is to promote safe, transparent, and human-centered collaboration between workers and intelligent systems, while mitigating ergonomic risks and supporting safe and sustainable industrial innovation.We welcome original research articles, reviews, methods papers, and applied case studies that address ergonomics and human factors in human–agent collaboration across industrial, healthcare, transportation, logistics, and service contexts. Submissions may include, but are not limited to, the following sub-themes:- Multimodal sensing and data fusion for assessing physical and cognitive ergonomics during human–robot or human–AI interaction- Explainable AI (XAI) methods for ergonomic assessment, workload estimation, and human-state modeling- Small human dataset challenges, including transfer learning, data augmentation, and hybrid human–AI modeling strategies- Real-time monitoring and adaptive ergonomics in collaborative and semi-autonomous systems- Ethical, legal, and privacy considerations in continuous human monitoring, including data governance and regulatory implications- Design guidelines, standards, and policy perspectives for human-centered intelligent work systems Topic editors: Qian Zhang, Hongbo Zhang, Jie Xu Source: https://www.frontiersin.org/research-topics/77971/human-agent-collaborative-ergonomics-multimodal-measurement-explainable-ai-and-ethical-governance