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CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION

The ability for a computer to recognize emotions would have many uses. In the field of human-computer interaction, it would be useful if computers could sense if a user is frustrated and offer help (Lisetti & Nasoz, 2002), or it could be used in cars to predict stress or road rage (Nasoz, Lisetti, & Vasilakos, 2010). Also, it has uses in the medical field with emotional therapy or monitoring patients (Rebenitsch, Owen, Brohil, Biocca, & Ferydiansyah, 2010). Emotion recognition is a complex subject that combines psychology and computer science, but it is not a new problem. When the question was first posed, researchers examined at physiological signals that could help differentiate an emotion (Schachter & Singer, 1962). As the research progressed, researchers examined ways in which computers could recognize emotions, many of which were successful. Previous research has not yet looked at the emotional data as streaming data, or attempted to classify emotion in real time. This thesis extracts features from a window of simulated streaming data to attempt to classify emotions in real time. As a corollary, this method can also be used to attempt to identify the earliest point an emotion can be predicted. The results show that emotions can be classified in real time, and applying a window and feature extraction leads to better classification success. It shows that this method may be used to determine if an emotion could be predicted before it is cognitively experienced, but it could not predict the emotion transitional state. More research is required before that goal can be achieved.

Identiferoai:union.ndltd.org:MONTANA/oai:etd.lib.umt.edu:etd-01152013-202507
Date13 February 2013
CreatorsElmore, Nathan J.
ContributorsDr. Douglas Raiford, Dr. Min Chen, Dr. Allen D. Szalda-Petree
PublisherThe University of Montana
Source SetsUniversity of Montana Missoula
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.umt.edu/theses/available/etd-01152013-202507/
Rightsunrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Montana or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.

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