Manufacturing workers fatigue: an exploratory study on predictive machine learning and cross-subject generalization with implications for work design

Manufacturing workers’ fatigue is an acknowledged concern with implications for well-being, health, safety, and operational performance. Past studies have employed physiological measurements obtained from smartwatches and wearable devices, seeking to assess and classify the fatigue state of workers. However, the extent to which models developed based on data obtained from individual workers could apply to other workers remains unclear. This paper presents the results of an exploratory study in which data from different subjects are employed to develop a range of fatigue estimation and predictive machine learning models. A cross-subject study provides evidence of sufficiently accurate performance in several cases. Further insights arise from looking into cases of lower generalization and linking these to personal characteristics.