Earticle

초록

영어

Real-time emotion recognition technology is required for human-robot interaction. There is a method using the Somatic Nervous System (SNS). The PPG signal, which is easy to acquire data and can be used by dividing it into pulse units, is easy for real-time emotion recognition. However, each pulse label is the most important factor in segmenting and using the short-term 1-second to 3-second PPG signal. However, DEAP and MAHNOB-HCI are public bio-signal data, but DEAP Dataset only provides Self-Assessment Labeling for emotion-inducing videos of 60 seconds. The MAHNOB-HCI Database provides Annotation Labeling in which the observer sees the frontal image of the subject's face and measures emotion in real-time. Annotation labeling is effective to split the labels of PPG signals by pulse units. As a result of the experiment, Arousalbased positives 2415/neutral 3884/negative 4201, Valence-based positives 3570/neutral 2835/negative 4095.labels were drived. Annotation labeling allows us to check the emotions of participants in various distributions for 60 seconds. Analyzing the label setting and data preprocessing process contributes to improving short-term real-time emotion recognition.

목차

요약
1. 서론
2. 관련 연구
3. 제안한 방법
3.1. PPG 신호 전처리 및 분할
3.2. 공개 데이터 비교
4. 실험
4.1. 실험 환경
4.2. 실험 결과
5. 결론
참고문헌

키워드

저자

• 이하늘 [ Ha-Neul Lee | Department of Electronic Engineering, Inha University Incheon, Korea ]
• 강동현 [ Dong-Hyun Kang | Department of Electronic Engineering, Inha University Incheon, Korea ]
• 김덕환 [ Deok-Hwan Kim | Department of Electronic Engineering, Inha University Incheon, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004