Stress Detection Using Eye Activity via Recurrent Neural Networks

Chronic stress has become a significant issue in modern society, negatively affecting individuals' overall well-being and potentially leading to severe physiological and psychological disorders. Most existing methods for automatic stress detection rely on physiological data, which requires direct skin contact with sensors. Not only does this make them intrusive, but it also limits their practicality for everyday use due to accuracy constraints. In this paper, we propose a novel approach using eye data captured by a standard video camera -specifically, eye gaze direction and eye landmarks- for stress detection. We use two types of Recurrent Neural Networks (RNNs), namely a Long Short-Term Memory (LSTM) network and a Gated Recurrent Unit (GRU) network. Through experiments on the UBFC-Phys dataset, we show that our method achieves a stress detection accuracy of up to 83.69% using gaze data, while eye landmarks further improve accuracy to 90.12%. Combined, these two modalities reach accuracy of 90.83%. These results highlight the effectiveness of our approach in enabling accurate and non-intrusive stress detection.