The emotional state classification using physiological signal interpretation framework

© 2018 IEEE. This paper proposes and evaluates an emotional state classification using a physiological signal interpretation framework. The proposed Emo-CSI framework consists of three components which are the following: 1) physiological signal sensing, 2) data pre-processing, and 3) emotional state...

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Main Authors: Kitimapond Rattanadoung, Paskorn Champrasert, Somrawee Aramkul
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/58494
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-584942018-09-05T04:39:06Z The emotional state classification using physiological signal interpretation framework Kitimapond Rattanadoung Paskorn Champrasert Somrawee Aramkul Computer Science Medicine Physics and Astronomy © 2018 IEEE. This paper proposes and evaluates an emotional state classification using a physiological signal interpretation framework. The proposed Emo-CSI framework consists of three components which are the following: 1) physiological signal sensing, 2) data pre-processing, and 3) emotional state classification. The Emo-CSI framework applies physiological signals (i.e., heart rate, breathing pattern, skin temperature, skin humidity, and skin conductivity) to classify the emotional state. The emotional state classification results in an emotional state (i.e., displeasure, neutral, pleasure, calm, medium, and excited). This research also investigates the accuracy of three classification techniques which are the following: 1) support vector machine (SVM), 2) artificial neural network (ANN), and 3) decision tree (DT). The evaluation results show that the physiological signals are related to emotional state. Using SVM as a classification in Emo-CSI outperforms the other classification techniques. 2018-09-05T04:25:34Z 2018-09-05T04:25:34Z 2018-06-04 Conference Proceeding 2-s2.0-85049351679 10.1109/ICSIGSYS.2018.8373573 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049351679&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/58494
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Medicine
Physics and Astronomy
spellingShingle Computer Science
Medicine
Physics and Astronomy
Kitimapond Rattanadoung
Paskorn Champrasert
Somrawee Aramkul
The emotional state classification using physiological signal interpretation framework
description © 2018 IEEE. This paper proposes and evaluates an emotional state classification using a physiological signal interpretation framework. The proposed Emo-CSI framework consists of three components which are the following: 1) physiological signal sensing, 2) data pre-processing, and 3) emotional state classification. The Emo-CSI framework applies physiological signals (i.e., heart rate, breathing pattern, skin temperature, skin humidity, and skin conductivity) to classify the emotional state. The emotional state classification results in an emotional state (i.e., displeasure, neutral, pleasure, calm, medium, and excited). This research also investigates the accuracy of three classification techniques which are the following: 1) support vector machine (SVM), 2) artificial neural network (ANN), and 3) decision tree (DT). The evaluation results show that the physiological signals are related to emotional state. Using SVM as a classification in Emo-CSI outperforms the other classification techniques.
format Conference Proceeding
author Kitimapond Rattanadoung
Paskorn Champrasert
Somrawee Aramkul
author_facet Kitimapond Rattanadoung
Paskorn Champrasert
Somrawee Aramkul
author_sort Kitimapond Rattanadoung
title The emotional state classification using physiological signal interpretation framework
title_short The emotional state classification using physiological signal interpretation framework
title_full The emotional state classification using physiological signal interpretation framework
title_fullStr The emotional state classification using physiological signal interpretation framework
title_full_unstemmed The emotional state classification using physiological signal interpretation framework
title_sort emotional state classification using physiological signal interpretation framework
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049351679&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58494
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