Stress Biomarkers

Stress biomarkers research aims to objectively identify and quantify stress using physiological and behavioral signals, primarily to enable early intervention and personalized stress management. Current research focuses on developing accurate and personalized stress detection models using wearable sensor data (e.g., heart rate variability, electrodermal activity) and machine learning techniques, including deep learning, active reinforcement learning, and hybrid approaches combining handcrafted and learned features. These advancements hold significant promise for improving mental health assessments and interventions, particularly through the development of user-friendly, real-time monitoring systems.