Mental Stress Detection Using Artificial Intelligence Models

Abstract

Abstract Stress is a natural and common occurrence in humans. It leads to the release of hormones which help deal with the situation, but chronic stress affects our health and could lead to deleterious effects like depression, insomnia or headaches and therefore, early detection of stress becomes imperative to prevent such harmful consequences. This manuscript aims to automate the process of mental stress detection and help classify a stressed individual from a normal one through the use of physiological data collected from a wearable device. A publicly available dataset was used to evaluate our solution. Different Artificial Intelligence models like Artificial Neural Network (ANN), Hybrid of Artificial Neural Network and Support Vector Machine (ANN-SVM), Stacking Classifier and Radial Basis Function (RBF) Network were used, and their performance was compared using the accuracy of predicting correct stress state. During the study, Stacking Classifier gave the highest accuracy value of 99.92% while the RBF gave the least accuracy of 84.46% for three class classification of stress. The obtained results indicate the effectiveness of the proposed models in continuous monitoring of mental stress. The experimental results serve to demonstrate that the physiological signals can have a significant appositeness in mental stress detection.