Measurement of Operator Stress and Workload in Industry 5.0: A Methodological Framework for Multimodal Sensing and Ethical Considerations

The transition toward Industry 5.0 emphasizes the need for human-centric manufacturing systems that place operator wellbeing at the center of industrial processes. In this context, the objective and reliable assessment of operator stress and workload represents a key challenge for ensuring both worker health and production reliability. This paper proposes a metrological framework for the multimodal measurement and analysis of operator stress and workload in industrial environments. The proposed approach integrates subjective questionnaires, wearable physiological sensing, behavioral motion monitoring, and environmental measurements to capture complementary aspects of operator state during task execution. The framework is structured as a processing pipeline that includes co-design, sensors selection, data preprocessing, feature extraction, and artificial intelligence-based analysis. In addition, a set of Key Performance Indicators (KPIs) is defined to quantitatively evaluate the accuracy and robustness of fatigue detection, activity recognition, gaze estimation, posture analysis, and intention prediction. The identification of stress and workload conditions enables informed decision-making strategies aimed at improving working conditions and optimizing production performance. In particular, the proposed framework supports the detection of critical conditions associated with excessive physical or cognitive load, enabling targeted interventions such as ergonomic adjustments, workload redistribution, and environmental optimization. The methodology is designed according to ethical-by-design principles, ensuring privacy-preserving data management, transparency, and compliance with current regulations on data protection and trustworthy artificial intelligence. By combining multimodal sensing, data-driven analysis, and human-centric design principles, the proposed framework contributes to the development of reliable and scalable monitoring systems aligned with the Industry 5.0 paradigm.