Stress Detection

Stress detection research aims to accurately identify stress levels using various physiological signals (e.g., heart rate variability, electrodermal activity, facial expressions) and contextual data, often employing machine learning models like deep neural networks (including convolutional and recurrent architectures), and graph neural networks for analysis. Current research emphasizes improving model accuracy and generalizability across diverse populations and contexts, including personalization through techniques like self-supervised learning and transfer learning, and addressing privacy concerns through federated learning and synthetic data generation. This field holds significant potential for improving mental health monitoring, enabling early intervention strategies, and informing the design of stress-reducing interventions in various settings.