Stress Detection Model

Stress detection models aim to automatically identify stress levels using physiological signals (e.g., heart rate variability, electrodermal activity) and other data sources, primarily leveraging machine learning techniques such as support vector machines, neural networks, and gradient boosting. Current research emphasizes improving model generalizability across diverse datasets and individuals, focusing on factors like stressor type and the use of multimodal data and self-supervised learning to address data scarcity and individual variability. These advancements hold significant potential for improving mental health monitoring and intervention strategies, particularly through the use of readily available consumer-grade wearable sensors.