Hybrid genetic algorithms for stress recognition in reading

Stressis a major problem facing our world today and affects everyday lives providing motivation to develop an objective understanding of stress during typicalactivities. Physiological and physical response signals showing symptoms for stress can be used to provide hundreds of features. This encounters the problem of selecting appropriate features for stress recognition from a set of features that may include irrelevant, redundant or corrupted features. In addition, there is also a problem for selecting an appropriate computational classification model with optimal parameters to capture general stress patterns. The aim of this paper is to determine whether stress can be detected from individual-independent computational classification models with a genetic algorithm (GA) optimization scheme from sensor sourced stress response signals induced by reading text. The GA was used to select stress features, select a type of classifier and optimize the classifier's parameters for stress recognition. The classification models used were artificial neural networks (ANNs) and support vector machines (SVMs). Stress recognition rates obtained from an ANN and a SVM without a GA were 68% and 67% respectively. With a GA hybrid, the stress recognition rate improved to 89%. The improvement shows that a GA has the capacity to select salient stress features and define an optimal classification model with optimized parameter settings for stress recognition.