Stress Datasets

Stress datasets are collections of physiological and/or behavioral data used to train and evaluate machine learning models for stress detection. Current research focuses on improving the generalizability of these models across diverse datasets, investigating the influence of factors like stressor type and sensor modality, and exploring various machine learning architectures, including deep learning and ensemble methods, to enhance accuracy and robustness. This work is significant because accurate stress detection has broad implications for mental health monitoring, personalized interventions, and improving the design of stress-reducing technologies. The development of more comprehensive and representative datasets is crucial for advancing this field.