Issues and Challenges in Detecting Mental Stress from Multimodal Data Using Machine Intelligence

The human body responds to challenging events or demanding conditions by entering an escalated psycho-physiological state known as stress. Psychological stress can be captured through physiological signals (ECG, EEG, EDA, GSR, etc.) and behavioral signals (eye gaze, head movement, pupil diameter, etc.) Multiple stressors occurring simultaneously can cause adverse mental and physical health effects, leading to chronic health issues. Continuous monitoring of stress using wearable devices promises real-time data collection, enabling early detection and prevention of stress-related issues. The present study presents an extensive overview of stress detection techniques utilizing wearable sensors, and machine & deep learning methods. Various types of machine learning algorithms, such as logistic regression, support vector machines, random forest, and deep learning models, have been applied to analyze data and detect stress. Additionally, the paper highlights the stress detection datasets and research conducted in different areas. The challenges and obstacles related to detecting stress using machine intelligence are also addressed in the paper.