SmartCar: Detecting Driver Stress
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Automatic Feature Localization in Thermal Images for Facial Expression Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
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
Do physiological data relate to traditional usability indexes?
OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal feature selection for support vector machines
Pattern Recognition
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
Expert Systems with Applications: An International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Thermal Analysis of Facial Muscles Contractions
IEEE Transactions on Affective Computing
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 health problem in our world today. For this reason, it is important to gain an objective understanding of how average individuals respond to real-life events they observe in environments they encounter. The aims of this paper are to introduce the concept of observer stress and investigate whether a computational model can be developed to recognize observer stress using physiological and physical response sensor signals. The paper discusses the motivations for the investigation and details the experiments for data collection for observers of real-life settings which used unobtrusive methods suited to real-life environments. It describes an individual-independent support vector machine based model classifier to recognize stress patterns from observer response signals. A genetic algorithm is used for feature selection to build a classifier. The classifier recognized observer stress with an accuracy of 98%. The outcomes of this research provide a new application area for knowledge discovery and data mining to predict human stress response to real-life environments and a possible future extension on managing stress objectively.