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

Abstract:
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.