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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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References

  1. Scassellati B, Admoni H, Matarić M (2012) Robots for use in autism research. Annu Rev Biomed Eng 14:275–294

    Article  Google Scholar 

  2. Taheri AR, Alemi M, Meghdari A, PourEtemad HR, Basiri NM (2014) Social robots as assistants for autism therapy in Iran: research in progress. In: 2014 second RSI/ISM international conference on robotics and mechatronics (ICRoM). IEEE, pp 760–766

  3. Ingersoll B (2008) The social role of imitation in autism: implications for the treatment of imitation deficits. Infants Young Child 21(2):107–119

    Article  Google Scholar 

  4. Carpenter M, Pennington BF, Rogers SJ (2002) Interrelations among social-cognitive skills in young children with autism. J Autism Dev Disord 32(2):91–106

    Article  Google Scholar 

  5. Uzgiris I (1999) Imitation as activity: its developmental aspects. In: Nadel J, Butterworth G (eds) Imitation in infancy, ch. 6. Cambridge University, Cambridge, England, pp 186–206, ISBN: ISBN-0-521-58033-1. https://eric.ed.gov/?id=ED433967

  6. Cabibihan JJ, Javed H, Ang M, Aljunied SM (2013) Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism. Int J Soc Robot 5(4):593–618

    Article  Google Scholar 

  7. Taheri A, Meghdari A, Alemi M, Pouretemad H (2018) Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates. Int J Social Robot 10(1):93–113

    Article  Google Scholar 

  8. Kajopoulos J, Wong AHY, Yuen AWC, Dung TA, Kee TY, Wykowska A (2015) Robot-assisted training of joint attention skills in children diagnosed with autism. In: International conference on social robotics. Springer, Cham, pp 296–305

  9. Shukla J, Cristiano J, Oliver J, Puig D (2019) Robot assisted interventions for individuals with intellectual disabilities: impact on users and caregivers. Int J Soc Robot 11(4):631–649

    Article  Google Scholar 

  10. Ismail LI, Verhoeven T, Dambre J, Wyffels F (2019) Leveraging robotics research for children with autism: a review. Int J Soc Robot 11(3):389–410

    Article  Google Scholar 

  11. Diehl JJ, Schmitt LM, Villano M, Crowell CR (2012) The clinical use of robots for individuals with autism spectrum disorders: a critical review. Res Autism Spectr Disord 6(1):249–262

    Article  Google Scholar 

  12. Pennisi P, Tonacci A, Tartarisco G, Billeci L, Ruta L, Gangemi S, Pioggia G (2016) Autism and social robotics: a systematic review. Autism Res 9(2):165–183

    Article  Google Scholar 

  13. Pour AG, Taheri A, Alemi M, Meghdari A (2018) Human–robot facial expression reciprocal interaction platform: case studies on children with autism. Int J Soc Robot 10(2):179–198

    Article  Google Scholar 

  14. Costa AP, Charpiot L, Lera FR, Ziafati P, Nazarikhorram A, van der Torre L, Steffgen G (2018) A comparison between a person and a robot in the attention, imitation, and repetitive and stereotypical behaviors of children with autism spectrum disorder. In: Proceedings workshop on social human–robot interaction of human-care service robots at HRI2018

  15. So WC, Wong MY, Cabibihan JJ, Lam CY, Chan RY, Qian HH (2016) Using robot animation to promote gestural skills in children with autism spectrum disorders. J Comput Assist Learn 32(6):632–646

    Article  Google Scholar 

  16. Ali S, Mehmood F, Dancey D, Ayaz Y, Khan MJ, Naseer N et al (2019) An adaptive multi-robot therapy for improving joint attention and imitation of ASD children. IEEE Access 7:81808–81825

    Article  Google Scholar 

  17. Aly A (2020) Human posture recognition and gesture imitation with a humanoid robot. arXiv:2002.01779

  18. Fujimoto I, Matsumoto T, De Silva PRS, Kobayashi M, Higashi M (2011) Mimicking and evaluating human motion to improve the imitation skill of children with autism through a robot. Int J Soc Robot 3(4):349–357

    Article  Google Scholar 

  19. Pirsiavash H, Vondrick C, Torralba A (2014) Assessing the quality of actions. In: European conference on computer vision. Springer, Cham, pp 556–571

  20. Zariffa J, Kapadia N, Kramer JL, Taylor P, Alizadeh-Meghrazi M, Zivanovic V et al (2011) Relationship between clinical assessments of function and measurements from an upper-limb robotic rehabilitation device in cervical spinal cord injury. IEEE Trans Neural Syst Rehabil Eng 20(3):341–350

    Article  Google Scholar 

  21. Daunoravičienė K, Žižienė J, Pauk J, Idzkowski A, Raudonytė I, Juocevičius A et al (2017) Stroke-affected upper extremity movement assessment via continuous relative phase analysis. Measurement 110:84–89

    Article  Google Scholar 

  22. Bosecker C, Dipietro L, Volpe B, Igo Krebs H (2010) Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabil Neural Repair 24(1):62–69

    Article  Google Scholar 

  23. Butler EE, Ladd AL, LaMont LE, Rose J (2010) Temporal–spatial parameters of the upper limb during a reach & grasp cycle for children. Gait Posture 32(3):301–306

    Article  Google Scholar 

  24. Zhou L, Bai S, Hansen MR, Rasmussen J (2011) Modeling of human arm energy expenditure for predicting energy optimal trajectories. Model Identif Control 31(3):91–101. https://doi.org/10.4173/mic.2011.3.1

  25. Balasubramanian S, Melendez-Calderon A, Roby-Brami A, Burdet E (2015) On the analysis of movement smoothness. J Neuroeng Rehabil 12(1):112

    Article  Google Scholar 

  26. Feng Y, Jia Q, Wei W (2018) A control architecture of robot-assisted intervention for children with autism spectrum disorders. J Robot 2018:3246708. https://doi.org/10.1155/2018/3246708

  27. Gordon AS (1995) Automated video assessment of human performance. In: Proceedings of AI-ED, pp 16–19

  28. Perše M, Kristan M, Perš J, Kovačič S (2007) Automatic evaluation of organized basketball activity using bayesian networks. na

  29. https://www.softbankrobotics.com/emea/en/nao (2020)

  30. Dong Y (2018) An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing. Aerosp Sci Technol 77:34–49

    Article  Google Scholar 

  31. Dong Y (2019) Implementing deep learning for comprehensive aircraft icing and actuator/sensor fault detection/identification. Eng Appl Artif Intell 83:28–44

    Article  Google Scholar 

  32. Rozo L, Silverio J, Calinon S, Caldwell DG (2016) Learning controllers for reactive and proactive behaviors in human–robot collaboration. Front Robot AI 3:30

    Article  Google Scholar 

  33. Calinon S, Guenter F, Billard A (2007) On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern Part B (Cybern) 37(2):286–298

    Article  Google Scholar 

  34. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  35. Duquette A, Michaud F, Mercier H (2008) Exploring the use of a mobile robot as an imitation agent with children with low-functioning autism. Auton Robots 24(2):147–157

    Article  Google Scholar 

  36. Giannopulu I, Montreynaud V, Watanabe T (2014) PEKOPPA: a minimalistic toy robot to analyse a listener-speaker situation in neurotypical and autistic children aged 6 years. In: Proceedings of the second international conference on human-agent interaction, pp 9–16

  37. Ricks DJ, Colton MB (2010) Trends and considerations in robot-assisted autism therapy. In: 2010 IEEE international conference on robotics and automation. IEEE, pp 4354–4359

  38. Pioggia G, Igliozzi R, Sica ML, Ferro M, Muratori F, Ahluwalia A, De Rossi D (2008) Exploring emotional and imitational android-based interactions in autistic spectrum disorders. J Cyber Ther Rehabil 1(1):49–61

    Google Scholar 

  39. Ingersoll B (2010) Brief report: pilot randomized controlled trial of reciprocal imitation training for teaching elicited and spontaneous imitation to children with autism. J Autism Dev Disord 40(9):1154–1160

    Article  Google Scholar 

  40. Taheri A, Meghdari A, Alemi M, Pouretemad H (2018) Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates. Int J Soc Robot 10(1):93–113

    Article  Google Scholar 

  41. Taheri A, Meghdari A, Alemi M, Pouretemad H (2019) Teaching music to children with autism: a social robotics challenge. Sci Iran 26(Special Issue on: Socio-Cognitive Engineering):40–58

    Google Scholar 

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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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  3. Mohammad H. Mahoor
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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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