Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities

To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physiological measures for monitoring work-related stress, electrooculography (EOG) remains underexplored in this context. Although less extensively studied, EOG shows significant promise for comparable applications. Furthermore, the realm of human factors and ergonomics lacks sufficient research on the integration of wearable sensors, particularly in the evaluation of human work. This article aims to bridge these gaps by examining the potential of EOG signals, captured through smart eyewear, as indicators of stress. The study involved twelve subjects in a controlled environment, engaging in four stress-inducing tasks interspersed with two-minute relaxation intervals. Emotional responses were categorized both into two classes (relaxed and stressed) and three classes (relaxed, slightly stressed, and stressed). Employing supervised machine learning (ML) algorithms-Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)-the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. The proposed wearable system shows promise in monitoring workers' well-being, especially during visual activities.