Detecting Industrial Boredom: An In-the-Wild Study of Monotonous Manufacturing Work Using Physiological Signals

Abstract

Blue-collar workers often face fragmented and routine work tasks that require attention but provide little stimulation. Over time, repetitive work can lead to cognitive underload, monotony, and boredom, which are considered stressors and have been shown to decrease productivity and job satisfaction. Despite the significant impacts of industrial monotony, the mechanisms behind boredom remain debated, as its perception varies among individuals. We investigated the potential of affective computing to provide new insights into industrial monotony and boredom. Our in-the-wild study with industrial workers (N=11) reveals that tasks perceived as boring differ from exciting work based on their physiological response, with electrodermal activity providing statistically significant features to distinguish them apart. Furthermore, our work demonstrates that neural networks can detect industrial boredom directly from raw physiological data. We discuss our findings and potential future applications for mitigating boredom in industry.