Assessing the Impact of Human-Robot Collaboration on Stress Levels and Cognitive Load in Industrial Assembly Tasks

Collaborative robots are becoming increasingly popular in various industrial contexts, thanks to their ability to safely support workers in heavy and repetitive tasks, without renouncing the human’s decision-making capacity and dexterity. However, their adoption raises several questions about their net effect on cognitive load reduction for workers, since physical closeness and interaction with a moving machine may be a source of psychological stress. In this work, we propose a multi-modal approach, based on physiological signals, for assessing the stress induced by human-robot collaboration in an industrial assembly task. The obtained results, supported by statistical and regression analyses, support the hypothesis that human-robot collaboration does not raise workers’ stress levels in collaborative assembly tasks; moreover, they show that the considered physiological signals are a promising means to predict such stress levels by using appropriate machine learning methods.