Transforming neurophysiological data into measurable stress metric using machine learning-based stress quantification framework for smart factory workers

Mental stress is a common issue in demanding occupational setups, such as smart industrial settings, particularly when working with robots, being one of the primary reasons for decreased performance and productivity. Estimating and evaluating stress are critical for worker safety, performance, and overall well-being. A novel integrated framework is proposed in this research for continuous cognitive stress-level estimation that combines neuroimaging (EEG and fNIRS), gaze tracking and machine learning.

A factory workers' stress assessment experiment is designed and implemented, which employs physiological, behavioral and subjective measures to assess mental stress from different perspectives. Physiological features extracted from multimodal neuroimaging data are used for training supervised classification and regression models, while gaze, behavioral, and subjective measures provide complementary evaluation of worker stress. To further optimize the pipeline, multiple feature selection algorithms are tested, followed by ensemble learning approaches, and the best model is chosen for continuous stress-level estimation.

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 behavioral 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 estimate continuous cognitive stress levels, utilizing the proposed framework presented in this research, thereby bridging the gap between research and practical application. The proposed multi-domain framework provides a promising solution for monitoring worker well-being in occupational settings.