Exploring Early Stress Detection from Multimodal Time Series with Deep Reinforcement Learning

In our fast-paced world, timely access to information is essential. This urgency is highlighted in stress detection, where swift actions can mitigate harmful psycho-physiological effects. We introduce an early stress detection method using Deep Reinforcement Learning (DRL). This method utilizes DRL to efficiently analyze time series data segments, aiming for accurate and quick stress classification. We employ a dynamic observation window strategy, allowing the DRL agent to adjust based on data complexity. Our evaluations, performed on a public dataset using a Leave-One-Subject-Out (LOSO) method, emphasize DRL's potential in stress detection. The related code is available at https://github.com/cosbidev/DRL-4-Early-Stress-Detection.