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Recognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression

DC Field Value Language
dc.contributor.author유선국-
dc.date.accessioned2020-04-13T16:46:43Z-
dc.date.available2020-04-13T16:46:43Z-
dc.date.issued2020-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/175506-
dc.description.abstractNegative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion changes over time and is affected by mood. Therefore, we measured electrocardiogram (ECG), skin temperature (ST), and galvanic skin response (GSR) to detect objective indicators. We also compressed the features associated with emotion using a stacked auto-encoder (SAE). Finally, the compressed features and time information were used in training through long short-term memory (LSTM). As a result, the proposed LSTM used with the feature compression model showed the highest accuracy (99.4%) for recognizing negative emotions. The results of the suggested model were 11.3% higher than with a neural network (NN) and 5.6% higher than with SAE.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI-
dc.relation.isPartOfSENSORS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleRecognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학교실)-
dc.contributor.googleauthorJeeEun Lee-
dc.contributor.googleauthorSun K. Yoo-
dc.identifier.doi10.3390/s20020573-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ03219-
dc.identifier.eissn1424-8220-
dc.identifier.pmid31968700-
dc.subject.keywordLSTM-
dc.subject.keywordauto-encoder-
dc.subject.keywordbio-signal-
dc.subject.keywordemotion-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthor유선국-
dc.citation.volume20-
dc.citation.number2-
dc.citation.startPageE573-
dc.identifier.bibliographicCitationSENSORS, Vol.20(2) : E573, 2020-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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