Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Galvanic skin response (GSR) as an index of cognitive load
CHI '07 Extended Abstracts on Human Factors in Computing Systems
An HRV Patterns Discovering Neural Network for Mobile Healthcare Services
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
License to chill!: how to empower users to cope with stress
Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Online discovery of Heart Rate Variability patterns in mobile healthcare services
Journal of Systems and Software
Facial Expression Analysis for Predicting Unsafe Driving Behavior
IEEE Pervasive 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
Hybrid chromosome genetic algorithm for generalized traveling salesman problems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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Some response signals being modeled for humans over some time segments may not be relevant for analysis and modeling. These signals could contribute to reducing the quality of patterns captured by models, inefficient processing and may impose huge demands on storage resources. This work proposes an approach to search for relevant time segments from human response signals particularly, physiological and physical signals to recognize stress. The paper proposes an approach to determine time segments that were critical to differentiate the types of text based on stress. A support vector machine (SVM) was used to classify the different types of text based on the features of the response signals. A SVM and genetic algorithm (GA) hybrid approach is developed to determine optimal time segments for stress detection (OTSSD). As well as optimizing time segments, the GA also dealt with hundreds of stress features that may have included redundant and irrelevant features. Optimal time segments for stress in reading were successfully found and the GA and SVM hybrid classifier showed an improvement in stress recognition when optimized features from the critical time segments were used.