Understanding workers’ psychological states and physiological responses during human–robot collaboration

The integration of collaborative robots (cobots) in manufacturing industries has triggered special attention toward human operators and their cognitive, affective, and behavioral responses to the collaboration with these robots. In this framework, the number of metrics and the interrelated nature of the cognitive phenomena they may reflect (e.g., workload, stress, fatigue, affective state) leaves open the question of what measures better respond to such cognitive fluctuations and whether the integration of multiple physiological signals increases their predictive power in capturing the operators’ mental state. This study addresses these topics by exploring the impact of industrial HRC on human workload, mental fatigue, and stress through a multi-method approach combining subjective (i.e., self-reported workload, fatigue, and stress), behavioral (i.e., performance speed and errors), and physiological measures (i.e., Heart Rate Variability and Pupillometry). The findings highlight the importance of choosing combined or single physiological measures based on specific constructs of interest and evidence the advantages of using a multi-method approach for more accurate monitoring of these psychophysical states, contributing to refining existing methods and advancing theoretical understanding.