Abstract
The paper investigates classification of stress in reading for males and females based on an artificial neural network model (ANN). An experiment was conducted, with stressful and non-stressful reading material as stimuli, to obtain galvanic skin response (GSR) signals, a good indicator of stress. GSR signals formed the input of the ANN with stressed and non-stressed states as the two output classes. Results show that stress in reading for males compared to females are significantly different (p < 0.01), with males showing different patterns in GSR signals to females.
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Sharma, N., Gedeon, T. (2011). Stress Classification for Gender Bias in Reading. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_39
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DOI: https://doi.org/10.1007/978-3-642-24965-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
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