NDLI: Neural network-based improvement in class separation of physiological signals for emotion classification

Content Provider	IEEE Xplore Digital Library
Author	Leon, E. Clarke, G. Sepulveda, F. Callaghan, V.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., Essex Univ. (Leon, E.; Clarke, G.; Sepulveda, F.; Callaghan, V.)
Abstract	Computer scientists have been slow to become aware of the importance of emotion on human decisions and actions. Recently, however, a considerable amount of research has focused on the utilisation of affective information with the intention of improving both human-machine interaction and artificial humanlike inference models. It has been argued that valuable information could be obtained by analysing the way affective states and environment interact and affect human behaviour. A method to improve pattern recognition among four bodily parameters employed for emotion recognition is presented. The utilisation of autoassociative neural networks has proved to be a valuable mechanism to increase inter-cluster separation related to emotional polarity (positive or negative). It is suggested that the proposed methodology could improve performance in pattern recognition tasks involving physiological signals. Also, by way of grounding the immediate aims of our research, and providing an insight into the direction of our work, we provide a brief overview of an intelligent-dormitory test bed in which affective computing methods was applied and compared to non-affective agents
Starting Page	724
Ending Page	728
File Size	963209
Page Count	5
File Format	PDF
ISBN	0780386434
DOI	10.1109/ICCIS.2004.1460677
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-01
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Humans Pattern recognition Man machine systems Information analysis Emotion recognition Artificial neural networks Grounding Intelligent agent Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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