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Detecting behavior and cognitive states of operators collaborating with industrial robots in the field is among the primary objectives and challenges in current human-robot collaboration research. To achieve this goal, it is first essential to introduce dynamic elements of the industrial settings into the lab to examine how to effectively detect, read, and interpret operators' psychophysical reactions associated with complex and dynamic workflows. As a first step toward this goal, we designed a realistic collaborative assembly task, encompassing common manufacturing operations performed jointly by humans and cobots. The task involves sequential sub-operations in a practical workflow (manual screwing, pick-and-place, supported screwing) and dual-tasking simulating conditions of fatigue and high workload. Early results cover users' performance, mental workload, and affective reactions, along with acceptance, engagement, and participants' perception of the experimenter's influence on the task execution. We finally discuss how such an experimental approach represents a first step toward field-based interventions.
Nenna et al. (Mon,) studied this question.