Stress Detection by means of Stress Physiological Template


This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications.

Publication type: 
Published in: 
Third World Congress on Nature and Biologically Inspired Computing (NaBIC), 2011 Salamanca, Spain. Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on p.p. 131-136
Publication date: 
October 2011
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