Description

Mental stress is a critical problem in our modern society. This form of stress strongly affects our well being, and technology is needed to help us to manage health problems. The ability to automatically recognize a person’s mental stress can be fundamental in supporting stress and health management. Several supervised learning models were rigorously applied to automate stress recognition. Additionally, we demonstrate the effectiveness of physiology-based stress level classification (no stress, low stress and high stress). This study provides detailed insight regarding the effect of objective physiological measures on the validation of subjective self-ratings under a novel complex VR stress training system, which stimulates the further investigations of stress disorder recognition. The effectiveness of the proposed method is compared with standard classifiers like Gaussian Naive Bayes (GNB) and we have also used python web application(Flask) for predicting our result in web page.