Research Article
Modeling Stress Recognition in Typical Virtual Environments
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2013.252011, author={Nandita Sharma and Tom Gedeon}, title={Modeling Stress Recognition in Typical Virtual Environments}, proceedings={7th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2013}, month={5}, keywords={stress recognition support vector machines artificial neural networks genetic algorithms physiological signals physical signals eeg films}, doi={10.4108/icst.pervasivehealth.2013.252011} }- Nandita Sharma
Tom Gedeon
Year: 2013
Modeling Stress Recognition in Typical Virtual Environments
PERVASIVEHEALTH
ICST
DOI: 10.4108/icst.pervasivehealth.2013.252011
Abstract
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.
