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A Close Look at the Imitation Performance of Children with Autism and Typically Developing Children Using a Robotic System

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Abstract

Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment system, we present different mathematical models of the children’s imitation performance using some State-Image based algorithms including Acceptable Bound, Mahalanobis Distance, and Signals’ Cross-Correlations as well as Hidden Markov Models based on the time-dependent kinematics data of the participants’ joints. Among the different studied models, we observed that the “State-Image Acceptable Bound method with position, velocity, and acceleration features” is the best one. This method has a mean Pearson correlation of ~ 45%, which is fairly comparable to the related works (out of autism field) in assessing the quality of dynamic actions. Finally, for a treatment application of using artificial intelligence algorithms in automated evaluation of children’s behaviors as an unbiased and quantifiable measurement in HRI, we propose a reciprocal gross imitation human–robot interaction platform with the potential to aid in the cognitive rehabilitation of children with autism.

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Acknowledgements

Our profound gratitude goes to the “Center for the Treatment of Autistic Disorders (CTAD)” and its psychologists for their contributions to the clinical trials with the children with autism. We sincerely appreciate Prof. Minoo Alemi and Prof. Hamidreza Pouretemad for their consults during the study. This research was funded by Sharif University of Techology (Grant No. G980517) and the “Cognitive Sciences and Technology Council” (CSTC) of Iran (http://www.cogc.ir/) (Grant No. 95p22). We also appreciate “Dr. AliAkbar Siasi Memorial Grant Award” for the complementary support of the Social & Cognitive Robotics Laboratory.

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Authors and Affiliations

  1. Social and Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics and Automation (CEDRA), Sharif University of Technology, Tehran, Iran

    Alireza Taheri & Ali Meghdari

  2. Ritchie School of Engineering and Computer Science, Computer Vision and Social Robotics Lab, University of Denver, Denver, CO, USA

    Mohammad H. Mahoor

Authors
  1. Alireza Taheri
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  2. Ali Meghdari
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Correspondence to Alireza Taheri.

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Conflict of interest

Authors Alireza Taheri and Ali Meghdari has received research grants from “Sharif University of Technology” (Grant No. G980517) and the “Cognitive Sciences and Technology Council” (CSTC) of Iran (Grant No. 95p22), respectively. The author Mohammad H. Mahoor declares that he has no conflict of interest.

Ethical Approval

Ethical approval for the protocol of this study was provided by the Iran University of Medical Sciences (#IR.IUMS.REC.1395.95301469).

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Taheri, A., Meghdari, A. & Mahoor, M.H. A Close Look at the Imitation Performance of Children with Autism and Typically Developing Children Using a Robotic System. Int J of Soc Robotics 13, 1125–1147 (2021). https://doi.org/10.1007/s12369-020-00704-2

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