A Comprehensive Approach to Early Detection of Workplace Stress with Multi-Modal Analysis and Explainable AI

This study introduces a novel framework for stress detection, leveraging the synergy of physiological signals and facial expressions through advanced machine learning techniques. Employing a suite of models including Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Convolutional Neural Networks (CNNs) such as VGG16 and Custom CNN models, we undertake a comprehensive analysis across varied data durations. Our findings highlights the superiority of LSTM networks, which consistently outperform SVMs across metrics, particularly excelling in longer data sequences with notable improvements of 4 % in average in test accuracy, precision, recall, and F1 scores. This highlights the critical advantage of deep learning in capturing complex temporal patterns inherent in stress manifestations.