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Effect of Different Visual Stimuli on Joint Attention of ASD Children Using NAO Robot

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 953))

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Abstract

Interaction of autistic children with socially assistive robots have shown improvements in their impairments. This research focuses on one of the core impairments i.e. joint attention of children with autism spectrum disorder (ASD) using robot-mediated therapy. The study aims to compare the effect of different visual stimuli on joint attention. Two different visual cues i.e. rasta and blink by NAO robot are introduced. The evaluation parameters are total time for eye contact and number of eye contacts in response to generated stimuli. The experiment is conducted on 12 ASD children, 8 sessions for each cue over the period of 2 months. Each session consists of 8 trials of each category. This approach achieves 80.3% of accuracy for rasta while 65.1% for blink cue. The average eye contact time for rasta is 38.8 s and for blink is 32.4 s, signifying the effect of a prominent visual cue in improvement of joint attention.

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References

  1. Mittal, V.A., Walker, E.F.: Diagnostic and statistical manual of mental disorders. Psychiatry. Res. 189, 158 (2011)

    Article  Google Scholar 

  2. Simonoff, E., Pickles, A., Charman, T., Chandler, S., Loucas, T., Baird, G.: Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J. Am. Acad. Child Adolesc. Psychiatry. 47(8), 921 (2008)

    Article  Google Scholar 

  3. Ganz, M.L.: The lifetime distribution of the incremental societal costs of autism. Arch. Pediatr. Adolesc. Med. 161(4), 343 (2007)

    Article  Google Scholar 

  4. Warren, Z., Vehorn, A.C., Dohrmann, E., Newsom, C.R., Taylor, J.L.: Brief report: service implementation and maternal distress surrounding evaluation recommendations for young children diagnosed with autism. Autism. 17(6), 693 (2013)

    Article  Google Scholar 

  5. Damiano, L., Dumouchel, P.: Anthropomorphism in human-robot co-evolution. Front. Psychol. 9, 468 (2018)

    Article  Google Scholar 

  6. Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M.: KASPAR – a minimally expressive humanoid robot for human-robot interaction research. Appl. Bionics Biomech. 6, 369–397 (2009)

    Article  Google Scholar 

  7. Dorsey, R., Howard, A.M.: Examining the effects of technology-based learning on children with autism: a case study. In: 2011 IEEE 11th International Conference on Advanced Learning Technologies, p. 260 (2011)

    Google Scholar 

  8. Hashemi, J., Spina, T.V., Tepper, M., Esler, A., Morellas, V., Papanikolopoulos, N., Sapiro, G.: A computer vision approach for the assessment of autism-related behavioral markers. In: Development and Learning and Epigenetic Robotics (ICDL), 2012 IEEE International Conference (2012)

    Google Scholar 

  9. Bryson, S.E., Zwaigenbaum, L., McDermott, C., Rombough, V., Brian, J.: The autism observation scale for infants: scale development and reliability data. J. Autism Dev. Disord. 38, 731 (2008)

    Article  Google Scholar 

  10. Sial, S.B., Sial, M.B., Ayaz, Y., Shah, S.I.A., Zivanovic, A.: Interaction of robot with humans by communicating simulated emotional states through expressive movements. Intell. Serv. Robot. 9, 231 (2016)

    Article  Google Scholar 

  11. Zheng, Z., Das, S., Young, E.M., Swanson, A., Warren, Z., Sarkar, N.: Autonomous robot-mediated imitation learning for children with autism. In: Robotics and Automation (ICRA), 2014 IEEE International Conference (2014)

    Google Scholar 

  12. Zheng, Z., Young, E.M., Swanson, A., Weitlauf, A., Warren, Z., Sarkar, N.: Robot-mediated mixed gesture imitation skill training for young children with ASD. In: Advanced Robotics (ICAR), 2015 International Conference (2015)

    Google Scholar 

  13. Goodwin, M.S.: Enhancing and accelerating the pace of autism research and treatment the promise of developing innovative technology. Focus Autism Other Dev. Disabil. 23(2), 125–128 (2008)

    Article  Google Scholar 

  14. Kasari, C., Gulsrud, A., Wong, C., Kwon, S., Locke, J.: Randomized controlled caregiver mediated joint engagement intervention for toddlers with autism. J. Autism Dev. Disord. 40, 1045–1056 (2010)

    Article  Google Scholar 

  15. Bekele, E., Lahiri, U., Davidson, J., Warren, Z., Sarkar, N.: Development of a novel robot-mediated adaptive response system for joint attention task for children with autism. In: RO-MAN, 2011 IEEE (2011)

    Google Scholar 

  16. Kumazaki, H., Warren, Z., Swanson, A., Yoshikawa, Y., Matsumoto, Y., Ishiguro, H., Sarkar, N., Minabe, Y., Kikuchi, M.: Impressions of humanness for android robot may represent an endophenotype for autism spectrum disorders. J. Autism Dev. Disord. 48(2), 632 (2018)

    Article  Google Scholar 

  17. Robins, B., Dautenhahn, K., Dickerson, P.: From isolation to communication: a case study evaluation of robot assisted play for children with autism with a minimally expressive humanoid robot. In: 2009 Second International Conferences on Advances in Computer-Human Interactions, p. 205 (2009)

    Google Scholar 

  18. Warren, Z., Zheng, Z., Swanson, A., Bekele, E., Zhang, L., Crittendon, J.A., Weitlauf, A., Sarkar, N.: Can robotic interaction improve joint attention skills? J. Autism Dev. Disord. 45(11), 3726 (2015)

    Article  Google Scholar 

  19. Scassellati, B.: Quantitative metrics of social response for autism diagnosis. In: Rom. 2005. IEEE International Workshop on Robot and Human Interactive Communication, p. 585 (2005)

    Google Scholar 

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Correspondence to Sara Ali .

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Ali, S., Mehmood, F., Ayaz, Y., Asgher, U., Khan, M.J. (2020). Effect of Different Visual Stimuli on Joint Attention of ASD Children Using NAO Robot. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_48

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