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다중 모달 생체신호를 이용한 딥러닝 기반 감정 분류

Other Titles
 Deep Learning based Emotion Classification using Multi Modal Bio-signals 
Authors
 이지은  ;  유선국 
Citation
 Journal of Korea Multimedia Society (멀티미디어학회논문지), Vol.23(2) : 146-154, 2020-02 
Journal Title
 Journal of Korea Multimedia Society (멀티미디어학회논문지) 
ISSN
 1229-7771 
Issue Date
2020-02
Keywords
Bio-signal ; Deep Learning ; Emotion
Abstract
Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.
Files in This Item:
T202001966.pdf Download
DOI
10.9717/kmms.2020.23.2.146
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179011
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