Unveiling the Mind: A Survey on Stress Detection Using Machine Learning and Deep Learning Techniques

Stress is a common psychological state that significantly impacts human well-being, productivity, and overall health. The ability to accurately detect stress in individuals is crucial for mitigating its harmful effects. In recent years, machine learning (ML) and deep learning (DL) have emerged as powerful tools for stress detection, utilizing physiological data, behavioral cues, and other relevant information. This survey paper provides a comprehensive review of the existing ML and DL approaches used for stress detection, exploring a wide range of models, datasets, and applications. We highlight various techniques, including supervised and unsupervised learning, feature extraction methods, and performance evaluation metrics. Additionally, the challenges in the field, such as data heterogeneity, real- time detection, and model interpretability, are discussed, along with future research directions that could further enhance the effectiveness of stress detection systems.