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Description

As cyber-physical systems continue to proliferate in Industry 4.0, ensuring operator safety in human-robot collaboration (HRC) has become increasingly critical. Collaborative industrial settings demand a thorough understanding of how operators and robots interact under varying cognitive demands. This study explores the dynamic relationships among EEG, GSR, and ECG signals during collaborative robotics tasks using a robust multimodal approach. By employing analytical techniques such as phase space plots, canonical correlation analysis (CCA), time series analysis and mini-batch K-Means clustering, the research reveals insights into workload transitions and cognitive stress in a typical industrial setting. These findings underscore the importance of integrating physiological signals to provide a comprehensive view of operator responses, enabling the development of adaptive systems that enhance safety and efficiency in real time within HRC environments

Publication Date

4-1-2025

Keywords

Cognitive load, Industry 4.0, workload monitoring, physiological signal analysis

Applying Cognitive Load Analysis and Physiological Signal Integration to Operator Safety in Human Robot Collaboration

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