Investigation of Individual Emotions with GSR and FTT by Employing LabVIEW

It is essential to distinguish between an imposter and a genuine emotion in certain applications. To facilitate this, the number of features is increased by incorporating physiological signals. Physiological changes in the human body cannot be pretended. Human emotional behavior changes the heart rate, skin resistance, finger temperature, EEG etc. These physiological signal parameters can be measured and included as the final feature vector. The network is to be trained considering all the feature points as inputs with a radial basis activation function at the hidden layer and a linear activation function at the output layer. The two physiological parameters galvanic skin response (GSR) and finger tip temperature (FTT) that are predominant in deciding the emotion of a person are considered in this chapter. The measurements made are transmitted to LabVIEW add-on card for further data processing and analysis. The results obtained are nearer to the reality with a good measure of accuracy.

[1]  Antonio Castro,et al.  What happens to the readiness potential when the movement is not executed? , 2005, Neuroreport.

[2]  Gilles Pourtois,et al.  Simultaneous recording of EEG and facial muscle reactions during spontaneous emotional mimicry , 2008, Neuropsychologia.

[3]  Denise A. Garofalo,et al.  Empires of the Future: Libraries, Technology, and the Academic Environment , 2016 .

[4]  Keigo Watanabe,et al.  Multi-robot team work with benevolent characters: The roles of emotions , 2009 .

[5]  Edward Iglesias.  Robots in Academic Libraries: Advancements in Library Automation , 2013 .

[6]  Christian Peter,et al.  Emotion representation and physiology assignments in digital systems , 2006, Interact. Comput..

[7]  J. Schulkin,et al.  Facial expressions of emotion: A cognitive neuroscience perspective , 2003, Brain and Cognition.

[8]  Hui Chen,et al.  Sliding mode control of a 2D torsional MEMS micromirror with sidewall electrodes , 2012, 2012 International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO).

[9]  Hatice Gunes,et al.  Automatic, Dimensional and Continuous Emotion Recognition , 2010, Int. J. Synth. Emot..

[10]  P. A. Vijaya,et al.  An Improved Artificial Neural Network Based Emotion Classification System for Expressive Facial Images , 2013 .

[11]  P. A. Vijaya,et al.  Galvanic Skin Response: A Physiological Sensor System for Affective Computing , 2013 .

[12]  Luis J. Manso,et al.  Interactive Games with Robotic and Augmented Reality Technology in Cognitive and Motor Rehabilitation , 2012 .

[13]  A. Damasio.  Descartes' error: emotion, reason, and the human brain. avon books , 1994 .

[14]  P. Vijaya,et al.  Emotion Recognition Using Finger Tip Temperature: First Step towards an Automatic System , 2012 .

[15]  Klaus R. Scherer,et al.  Investigating the production of emotional facial expressions: a combined electroencephalographic (EEG) and electromyographic (EMG) approach , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[16]  John G. Taylor,et al.  The interaction of attention and emotion , 2005, Neural Networks.

[17]  Arun Ross,et al.  Information fusion in biometrics , 2003, Pattern Recognit. Lett..

[18]  P. A. Vijaya,et al.  Analysis of Human Emotions Using Galvanic Skin Response and Finger Tip Temperature , 2011, Int. J. Synth. Emot..

[19]  U. Hess,et al.  Differentiating emotion elicited and deliberate emotional facial expressions , 1990 .

[20]  François Mauguière,et al.  Experiencing and detecting happiness in humans: The role of the supplementary motor area , 2006, Annals of neurology.