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- 발행일
- 201406
- 출처
- 제어로봇시스템학회논문지, v.20 no.6, pp.612-618
- ISSN
- 1976-5622
- 출판사
- 제어로봇시스템학회
- 협약과제
- 14PC1800, 인간 친화적 로봇 서비스 환경에서 판단 적합성 90%이상인 복합지식 기반 판단 및 의미기반 로봇 표현 기술 개발, 김재홍
- 초록
- A pattern classifier-based approach for recognizing internal states of human participants in interactions is presented along with its experimental results. The approach includes a step for collecting video recordings of human-human interactions or human-robot interactions and subsequently analyzing the videos based on human coded annotations. The annotation includes social signals directly observed in the video recordings and the internal states of human participants indirectly inferred from those observed social signals. Then, a pattern classifier is trained using the annotation data, and tested. In our experiments on human-robot interaction, 7 video recordings were collected and annotated with 20 social signals and 7 internal states. Several experiments were performed to obtain an 84.83% recall rate for interaction engagement, 93% for concentration intention, and 81% for task comprehension level using a C4.5 based decision tree classifier.
- KSP 제안 키워드
- Annotation data, Based Approach, Decision Tree(DT), Decision Tree Classifier, Human-Robot Interaction(HRI), Recall rate, Social Signals, Video recording, human interaction, internal states, pattern classifier