메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Myung-Won Lee (K-THEBOM Research Institute) Yeong-Hyeon Byeon (Chosun University) Chan-Uk Yeom (Chosun University) Keun-Chang Kwak (Chosun University)
저널정보
대한전기학회 전기학회논문지 P 전기학회논문지 제70P권 제3호
발행연도
2021.9
수록면
163 - 173 (11page)
DOI
10.5370/KIEE.2021.70.3.163

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
This paper describes the design of ensemble deep models based on time-scale transformation from electrocardiogram (ECG) signals for emotion recognition. As the number of senior citizens living alone increases, emotion robots that can interact with them and other emotion robots are becoming increasingly important. Existing emotion robots usually recognized emotion through images of the user’s facial expressions or voice signals. However, there are many situations where the user’s emotions cannot read under various environments. Therefore, research on recognizing the user’s emotions through ECG signals among different biomedical signals is actively being conducted. The proposed method converts ECG signals into various types of two-dimensional time-scale representations. We then designed a four-stream deep learning model by applying it to an ensemble form and transfer learning. Finally, an experiment was conducted using the ASCERTAIN sentiment database. This database contains data recorded by 58 people with 9 different emotions. Among these emotions, we used six representatives (surprise, happiness, anger, disgust, fear, and sadness). The experimental results revealed that the presented ensemble deep models showed good performance in comparison with each single deep model and the original model without transformation.

목차

Abstract
1. Introduction
2. Basic Characteristics of ECG and 2D Time-Scale Representations
3. Transfer Learning
4. Experimental Results
5. Conclusion
References

참고문헌 (52)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0