Machine Learning-Based Neuroergonomic Stress Quantification of Factory Workers in Human–Robot Collaboration Environments Using Multimodal Data

Mental stress is a common issue in demanding occupational setups, such as smart industrial settings, particularly from working with robots, being one of the primary reasons for decreased performance and productivity. Quantifying and evaluating stress are critical for worker safety, performance, and overall well-being. A novel integrated framework is proposed in this research for digitising and assessing cognitive stress that combines neuroimaging (EEG and fNIRS), gaze tracking and machine learning. A factory workers’ stress assessment experiment is designed and implemented, which employs physiological, behavioural and subjective measures to assess mental stress from different perspectives. Physiological features extracted from multimodal data are used for training supervised classification and regression models. To further optimise the pipeline, multiple feature selection algorithms are tested, followed by ensemble learning approaches, and the best one is chosen for stress prediction. After implementing the novel stress quantification framework for the factory workers' stress assessment experiment, the ensemble learning approach produced the best results for both regression (RMSE: 10.86) and classification (accuracy: 84.1%) techniques using the STAI score as the target. The behavioural and subjective measures demonstrate the effect of varying process variables (light, noise, task speed, and complexity) during the experiment. Multimodal data, machine learning, and other computational approaches are integrated in this study to objectively quantify cognitive stress, utilising the novel stress quantification framework presented in this research, thereby bridging the gap between research and practical application. This study proposes a multi-domain framework for measuring stress, providing a promising solution for worker well-being in occupational setups.