Stress Annotation

Stress annotation research focuses on automatically identifying and classifying stress from various data sources, aiming to improve mental health monitoring and related applications. Current efforts involve developing machine learning models, including neural networks and tree-based methods, trained on diverse data like physiological signals (ECG, EDA, thermal imaging), speech, and text. These models are being refined through techniques such as co-teaching, data augmentation, and semi-supervised learning to enhance accuracy and robustness, particularly in uncontrolled environments. The ultimate goal is to create reliable, real-time stress detection systems for applications ranging from personalized mental healthcare to workplace productivity assessment.