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Stress Classification for Gender Bias in Reading

Date

2011

Authors

Sharma, Nandita
Gedeon, Tamas (Tom)

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

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.

Description

Keywords

Keywords: Artificial neural network models; Galvanic skin response; Gender bias; reading; Classification (of information); Data processing; Electrophysiology; Neural networks artificial neural networks; classification; galvanic skin response; gender stress; reading

Citation

URI

http://hdl.handle.net/1885/17265

Collections

ANU Research Publications

Source

Neural Information Processing (LNCS 7064)

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

DOI

10.1007/978-3-642-24965-5_39

Restricted until

Downloads

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Description
01_Sharma_Stress_Classification_for_2011.pdf (336.74 KB)

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