Advancing Stress Detection with Machine Learning: A Study on Multimodal Data Integration

Stress detection is a critical area in mental health, impacting both individual well-being and productivity. Traditional methods of stress assessment are often subjective and time-consuming. Recent advancements in machine learning have opened new avenues for automatic stress detection using multimodal data fusion techniques, which integrate diverse data sources such as physiological signals, behavioral cues, and contextual information. This paper explores the state-of-the-art multimodal data fusion techniques for automatic stress detection, presenting a comprehensive literature review, detailed methodology, and an analysis of their efficacy. The study concludes by discussing the challenges, potential solutions, and future directions in the field.