Intention reading from a fuzzy-based human engagement model and behavioural features

Sang Seok Yun, Mun Taek Choi, Munsang Kim, Jae Bok Song

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article number49
JournalInternational Journal of Advanced Robotic Systems
Publication statusPublished - 2012 Aug 10


  • Focus of attention
  • Fuzzy min-max neural networks (FMMNN)
  • Gaze behaviour
  • Human-robot interaction
  • Intention reading
  • Multi-modal sensors

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence


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