Intention Reading from a Fuzzy-based Human Engagement Model and Behavioural Features Regular Paper
- Authors
- Yun, Sang-Seok; Choi, Mun-Taek; Kim, Munsang; Song, Jae-Bok
- Issue Date
- 10-8월-2012
- Publisher
- SAGE PUBLICATIONS INC
- Keywords
- Fuzzy min-max neural networks (FMMNN); gaze behaviour; focus of attention; human-robot interaction; intention reading; multi-modal sensors
- Citation
- INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, v.9
- Indexed
- SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
- Volume
- 9
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/107713
- DOI
- 10.5772/50648
- ISSN
- 1729-8814
- Abstract
- This paper presents a novel approach for a quantitative appraisal model to identify human intent so as to interact with a robot and determine an engagement level. To efficiently select an attention target for communication in multi-person interactions, we propose a fuzzy-based classification algorithm which is developed by an incremental learning procedure and which facilitates a multi-dimensional pattern analysis for ambiguous human behaviours. From acquired participants' non-verbal behaviour patterns, we extract the dominant feature data, analyse the generality of the model and verify the effectiveness for proper and prompt gaze behaviour. The proposed model works successfully in multiple people interactions.
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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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