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Longitudinal Impact of Autonomous Robot-Mediated Joint Attention Intervention for Young Children with ASD

  • Zhi ZhengEmail author
  • Guangtao Nie
  • Amy Swanson
  • Amy Weitlauf
  • Zachary Warren
  • Nilanjan Sarkar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9979)

Abstract

Literature suggests that a robot is able to capture attention and elicit positive social communication behaviors in many children with ASD. However, there are few studies reported regarding the longitudinal impact of autonomous robot-mediated interventions. In this paper, we introduce a new autonomous robotic system for teaching children with ASD joint attention skills, which is among the core developmental impairments in ASD. This system automatically tracks the participant’s behavior during intervention and adaptively adjusts the interaction pattern based on the participant’s performance. Based on this system, we report a longitudinal robot-mediated joint attention intervention on a user study for 6 children with ASD. First, four sessions of one-target interventions were conducted for each participant. Then we tested their joint attention skills after 8 months in two sessions on: (1) response to one target; (2) response to two targets. The results showed that this autonomous robotic system was able to elicit improved one-target joint attention performance in young children with ASD over the course of 8 months. The result also suggested that the joint attention skills that the participants practiced in the one-target sessions might help them interact in a more difficult task. The robot also attracted the participants’ attention constantly during this long term intervention.

Keywords

Robot-mediated joint attention skills training Children with ASD Longitudinal study 

Notes

Acknowledgement

This study was partially supported by: the Hobbs Grant from the Vanderbilt Kennedy Center, a Vanderbilt University Innovation and Discovery in Engineering and Science (IDEAS) grant, National Science Foundation Grant 1264462, and the National Institute of Health Grants 1R01MH091102-01A1and 5R21MH103518-02. This work also got core supports from NICHD (P30HD15052) and NCATS (UL1TR000445-06).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zhi Zheng
    • 1
    Email author
  • Guangtao Nie
    • 1
  • Amy Swanson
    • 2
  • Amy Weitlauf
    • 2
  • Zachary Warren
    • 2
  • Nilanjan Sarkar
    • 1
    • 3
  1. 1.Electrical Engineering and Computer Science DepartmentNashvilleUSA
  2. 2.Vanderbilt Kennedy Center Treatment and Research Institute for Autism Spectrum DisorderNashvilleUSA
  3. 3.Department of Mechanical EngineeringVanderbilt UniversityNashvilleUSA

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