Can Robotic Interaction Improve Joint Attention Skills?
- 1.7k Downloads
Although it has often been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorder (ASD), relatively few investigations have indexed the impact of intervention and feedback approaches. This pilot study investigated the application of a novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group (n = 6) of children with ASD. Across a series of four sessions, children improved in their ability to orient to prompts administered by the robotic system and continued to display strong attention toward the humanoid robot over time. The results highlight both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.
KeywordsAutism spectrum disorder Robotics Technology Joint attention
This study was supported by in part by a grant from the Vanderbilt Kennedy Center (Hobbs Grant), the Marino Autism Research Institute, a Vanderbilt University Innovation and Discovery in Engineering and Science (IDEAS) grant, the National Science Foundation under Grant 0967170, and the National Institute of Health under Grant 1R01MH091102-01A1. Work also includes core support from NICHD (P30HD15052) and NCATS (UL1TR000445-06). We gratefully acknowledge the contribution of the parents and children who took part in this study and the support of the clinical research staff of the AUTOSLAB and the Vanderbilt Kennedy Center Treatment and Research Institute for Autism Spectrum Disorders.
- Amendah, D., Grosse, S. D., Peacock, G., & Mandell, D. S. (2011). The economic costs of autism: A review. In D. Amaral, D. Geschwind, & G. Dawson (Eds.), Autisms (pp. 1347–1360). Oxford: Oxford University Press.Google Scholar
- American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed. text revision). Washington, DC, USA: Author.Google Scholar
- Bekele, E., Lahiri, U., Swanson, A., Davidson, J., Warren, Z., & Sarkar, N. (2012). A step towards developing adaptive robot-mediated intervention architecture (ARIA) for children with autism. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(2), 289–299.CrossRefPubMedGoogle Scholar
- Bekele, E., Lahiri, U., Swanson, A., Davidson, J., Warren, Z., & Sarkar, N. (2013). Pilot clinical application of an adaptive robotic system for young children with autism. Autism. (in press). Google Scholar
- Centers for Disease Control and Prevention (CDC). (2012). Prevalence of autism spectrum disorders—Autism and developmental disabilities monitoring network, United States, 2008. In MMWR surveillance summaries, 61, No. SS03, 1-19.Google Scholar
- Constantino, J., & Gruber, C. (2002). The social responsiveness scale. Los Angeles: Western Psychological Services.Google Scholar
- Dautenhahn, K., Werri, I., Rae, J., Dickerson, P., Stribling, P., & Ogden, B. (2002). Roboic playmates: Analysing interactive compentencies of children with autism playing with a mobile robot. In K. Dautenhahn, A. Bond, L. Canamero, et al. (Eds.), Socially intelligent agents creating relationships with computers and robots (pp. 117–124). The Netherlands: Kluwer.CrossRefGoogle Scholar
- Feil-Seifer, D., & Mataric, M. (2011). Automated detection and classification of positive vs. negative robot interactions with children with autism using distance-based features. In Proceedings of the 6th international conference (ACM/IEEE) on Human–robot interaction (pp. 323–330). New York, NY, USA: ACM Press.Google Scholar
- Gillesen, J. C., Barakova, E. I., Huskens, B. E, & Feijs, L. M. (2011). From training to robot behavior: Towards custom scenarios for robotics in training programs for ASD. Presented at the IEEE international conference on rehabilitation robotics rehab week, Zurich, Switzerland.Google Scholar
- Kozima, H., Nakagawa, C., & Yasuda, Y. (2005). Interactive robots for communication-care: A case-study in autism therapy. Presented at the IEEE international workshop on robot and human interactive communication, Nashville, TN, USA.Google Scholar
- Lord, C., Risi, S., Lambrecht, L., Cook, E., Leventhal, B., DiLavore, P., et al. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205–223.CrossRefPubMedGoogle Scholar
- Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (1999). Autism diagnostic observation schedule (ADOS). Los Angeles, CA, USA: Western Psychological Services.Google Scholar
- Michaud, F., & Théberge-Turmel, F. (2002). Mobile robotics toys and autism: Observations of interactions. In Boston & Dordrecht (Eds.), Socially intelligent agents creating relationships with computer s and robots (pp. 125–132). London: Kluwer.Google Scholar
- Mullen, E. M. (1995). Mullen scales of early learning. Circle Pines, MN, USA: American Guidance Service.Google Scholar
- Robins, B., Dautenhahn, K., & Dickerson, P. (2009). From isolation to communication: A case study evaluation of robot assisted play for children with autism with minimally expressive humanoid robot. Advances in Computer–Human Interactions, 205–211. doi: 10.1109/ACHI.2009.32.
- Rutter, M., Bailey, A., Lord, C., & Berument, S. (2003). Social communication questionnaire. Los Angeles, CA, USA: Western Psychological Services.Google Scholar
- Sasson, N. J., & Elison, J. T. (2012). Eye tracking youn children with autism. Journal of Visualized Experiments, 27(61), 3675. doi: 10.3791/3675.
- Warren, Z., Vehorn, A., Dohrmann, E., Newsom, C., & Taylor, J. L. (2012) Brief report: Service implementation and maternal distress surrounding evaluation recommendations for young children diagnosed with autism. Autism. doi: 10.1177/1362361312453881.
- Yoder, P. J., & McDuffie, A. S. (2006). Treatment of responding to and initiating joint attention. In T. Chairman & W. Stone (Eds.), Social and communication development in autism spectrum disorders: early identification, diagnosis, and intervention (pp. 88–114). New York, NY, USA: Guilford.Google Scholar