Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
License to chill!: how to empower users to cope with stress
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
Two Stress Detection Schemes Based on Physiological Signals for Real-Time Applications
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
IEEE Transactions on Fuzzy Systems
Understanding physiological responses to stressors during physical activity
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Computer Methods and Programs in Biomedicine
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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.