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Human–robot facial expression reciprocal interaction platform: case studies on children with autism

Ghorbandaei Pour, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/s12369-017-0461-4
  3. Publisher: Springer Netherlands , 2018
  4. Abstract:
  5. Reciprocal interaction and facial expression are some of the most interesting topics in the fields of social and cognitive robotics. On the other hand, children with autism show a particular interest toward robots, and facial expression recognition can improve these children’s social interaction abilities in real life. In this research, a robotic platform has been developed for reciprocal interaction consisting of two main phases, namely as Non-structured and Structured interaction modes. In the Non-structured interaction mode, a vision system recognizes the facial expressions of the user through a fuzzy clustering method. The interaction decision-making unit is combined with a fuzzy finite state machine to improve the quality of human–robot interaction by utilizing the results obtained from the facial expression analysis. In the Structured interaction mode, a set of imitation scenarios with eight different posed facial behaviors were designed for the robot. As a pilot study, the effect and acceptability of our platform have been investigated on autistic children between 3 and 7 years old and the preliminary acceptance rate of ∼ 78% is observed in our experimental conditions. The scenarios start with simple facial expressions and get more complicated as they continue. The same vision system and fuzzy clustering method of the Non-structured interaction mode are used for automatic evaluation of a participant’s gestures. Lastly, the automatic assessment of imitation quality was compared with the manual video coding results. The Pearson’s r on these equivalent grades were computed as r=0.89 which shows a sufficient agreement on the automatic and manual scores. © 2018, Springer Science+Business Media B.V., part of Springer Nature
  6. Keywords:
  7. Fuzzy finite state machine ; Human–robot interaction (HRI) ; Cluster analysis ; Decision making ; Diseases ; Face recognition ; Fuzzy clustering ; Fuzzy systems ; Quality control ; Robotics ; Video signal processing ; Autism ; Facial expressions ; Fuzzy finite state machines ; Imitation ; Reciprocal interaction ; Robot interactions ; Human robot interaction
  8. Source: International Journal of Social Robotics ; Volume 10, Issue 2 , April , 2018 , Pages 179-198 ; 18754791 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s12369-017-0461-4