Visual fatigue prediction using classification model based on physiological responses of occupants under office lightings

Appropriate management of visual fatigue is crucial for eye health as it significantly impacts life quality throughout an individual's life cycle. The shift to digital tasks in modern office environments has increased occupants' exposure to visual fatigue, leading to various social problems. This study aimed to develop visual fatigue prediction models based on physiological responses and classification algorithms. Experiments were conducted to collect physiological responses and subjective visual fatigue under various lighting environments. Collected data was refined and reorganized as predictor variables by different time windows and target variables by scales. Then, visual fatigue prediction models were developed using supervised machine learning algorithms (i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest). And improved by feature selection and adding subject-label variables. The resulting two-scale and three-scale visual fatigue prediction models demonstrated an average performance of 93.73 % and 94.64 % respectively. This research can contribute to reducing societal costs and enhancing productivity by proposing visual fatigue prediction models that help manage eye health in office environments.