Modeling stress recognition in typical virtual environments

Stress is a major problem in our world today motivating objective understanding of how average individuals respond to stress in a typical activities. The main aim for this paper is to determine whether stress can be recognized using individual-independent computational models from sensor based stress response signals induced by films with typical stressful content. Another aim is to determine whether a consumer electroencephalogram (EEG) sensor device, which is portable, less obtrusive and relatively inexpensive, can be used for stress recognition. A support vector machine and an artificial neural network based models were developed to recognize stress using various physiological and physical signals. The models produced stress classification with 95% accuracy. Using the data obtained from the consumer device, the models produced stress classification with 91% accuracy. Statistical analysis of the results showed that the classification results from the physiological and physical signals are not statistically different to the results from the consumer device implying that the consumer device can be used for recognizing stress in typical virtual environments.