Engagement Evaluation for Autism Intervention by Robots Based on Dynamic Bayesian Network and Expert Elicitation | Zendy

Yongli Feng | Zendy; Qingxuan Jia | Zendy; Ming Chu | Zendy; Wei Wei | Zendy

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Engagement Evaluation for Autism Intervention by Robots Based on Dynamic Bayesian Network and Expert Elicitation

Author(s) -

Yongli Feng,

Qingxuan Jia,

Ming Chu,

Wei Wei

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2754291

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Robots as therapy tools have been researched in intervention for children with autism. During the interaction between robots and autistic children, engagement is an important metric which can be used to express whether robot's behavior is suited to the current context. The evaluation of engagement is a key prerequisite to improve the autonomous ability of robots in intervention. In this paper, we propose a new model to evaluate the engagement of children with autism. The proposed model is developed based on the dynamic Bayesian network, and the parameters of the model are obtained by fuzzy logic and expert elicitation. After determining the input features and the classification of engagement, the topology of the model is established. Afterward, experts' opinions are collected based on linguistic variables. Based on triangular fuzzy number, the parameterization of the model is realized by fuzzification, aggregation, and defuzzification. Finally, the model is validated by experiment. The result demonstrates that proposed model satisfies the actual demands and the result of engagement classification can provide the input condition for the decision making of the robot.

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