A Systematic Analysis of Artificial Intelligence Based Models for Stress Detection

Abstract

If stress continues to exist, it can result in a variety of illnesses, physical and psychological. Psychological levels of stress need to be closely assessed to implement prompt and successful early treatment strategies. During the last 10 years, AI based stress detection has drawn a lot of spotlights because of its significance for preventive care and psychological wellness assessment. With an emphasis on data sets, algorithmic procedures and assessment procedures, this statistical study examines developments in stress detection based on AI approaches from 2016 to 2026. This investigation statistically examines developments in deep learning (DL), Machine learning (ML) and hybrid techniques. The findings show a notable trend towards sophisticated neural structures and multi-modal data integration, which improves performance measures. Moreover, variations in assessment processes and data sets standards are also highlighted in this study, which offers a statistical analysis of the state of the art and points out aspects that require more investigation into adaptable as well as dependable stress recognition methods.