Yun, Sang-Seok Choi, Mun-Taek Kim, Munsang Song, Jae-Bok 2024-01-20T14:30:27Z 2024-01-20T14:30:27Z 2021-09-04 2012-08 1729-8806 https://pubs.kist.re.kr/handle/201004/129039 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. English SAGE PUBLICATIONS INC Intention Reading from a Fuzzy-based Human Engagement Model and Behavioural Features Regular Paper Article 10.5772/50648 1 International Journal of Advanced Robotic Systems, v.9 International Journal of Advanced Robotic Systems 9 N scie scopus other 000309933500007 Robotics Robotics Article GAZE Fuzzy min-max neural networks (FMMNN) gaze behaviour focus of attention human-robot interaction intention reading multi-modal sensors