Advertisement

Attention shifting during child—robot interaction: a preliminary clinical study for children with autism spectrum disorder

  • Guo-bin Wan
  • Fu-hao Deng
  • Zi-jian Jiang
  • Sheng-zhao Lin
  • Cheng-lian Zhao
  • Bo-xun Liu
  • Gong Chen
  • Shen-hong Chen
  • Xiao-hong Cai
  • Hao-bo Wang
  • Li-ping Li
  • Ting Yan
  • Jia-ming ZhangEmail author
Article
  • 91 Downloads

Abstract

There is an increasing need to introduce socially interactive robots as a means of assistance in autism spectrum disorder (ASD) treatment and rehabilitation, to improve the effectiveness of rehabilitation training and the diversification of treatment, and to alleviate the shortage of medical personnel in mainland China and other places in the world. In this preliminary clinical study, three different socially interactive robots with different appearances and functionalities were tested in therapy-like settings in four different rehabilitation facilities/institutions in Shenzhen, China. Seventy-four participants, including 52 children with ASD, whose processes of interacting with robots were recorded by three different cameras, all received a single-session three-robot intervention. Data were collected from not only the videos recorded, but also the questionnaires filled mostly by parents of the participants. Some insights from the preliminary results were obtained. These can contribute to the research on physical robot design and evaluations on robots in therapy-like settings. First, when doing physical robot design, some preferential focus should be on aspects of appearances and functionalities. Second, attention analysis using algorithms such as estimation of the directions of gaze and head posture of a child in the video clips can be adopted to quantitatively measure the prosocial behaviors and actions (e.g., attention shifting from one particular robot to other robots) of the children. Third, observing and calculating the frequency of the time children spend on exploring/playing with the robots in the video clips can be adopted to qualitatively analyze such behaviors and actions. Limitations of the present study are also presented.

Key words

Human-robot interaction Robot-enhanced therapy Socially interactive robots Robot-mediated intervention 

CLC number

TP242.6 R748 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We thank the four rehabilitation facilities/institutions and their healthcare professionals for providing facilitation and assistance to our facilitators in this preliminary clinical study. We thank all the parents who took their children to participate in our study and supported us in many ways. We thank our guest students Shen-hong CHEN, Xiao-hong CAI, Hao-bo WANG, and Li-ping LI for their hard work in preparing and conducting this preliminary clinical study.

Supplementary material

11714_2019_1361_MOESM1_ESM.pdf (518 kb)
Supplementary material, approximately 228 KB.

References

  1. Baltrušaitis T, Robinson P, Morency LP, 2016. OpenFace: an open source facial behavior analysis toolkit. Proc IEEE Winter Conf on Applications of Computer Vision, p.1–10. https://doi.org/10.1109/WACV.2016.7477553
  2. Begum M, Serna RW, Yanco HA, 2016. Are robots ready to deliver autism interventions? A comprehensive review. Int J Soc Rob, 8(2):157–181. https://doi.org/10.1007/s12369-016-0346-y CrossRefGoogle Scholar
  3. Clabaugh C, Becerra D, Deng E, et al., 2018. Month-long, in-home case study of a socially assistive robot for children with autism spectrum disorder. Companion of the ACM/IEEE Int Conf on Human-Robot Interaction, p.87–88. https://doi.org/10.1145/3173386.3177018
  4. Coeckelbergh M, Pop C, Simut R, et al., 2016. A survey of expectations about the role of robots in robot-assisted therapy for children with ASD: ethical acceptability, trust, sociability, appearance, and attachment. Sci Eng Ethics, 22(1):47–65. https://doi.org/10.1007/s11948-015-9649-x CrossRefGoogle Scholar
  5. Costescu CA, Vanderborght B, David DO, 2014. The effects of robot-enhanced psychotherapy: a meta-analysis. Rev Gen Psychol, 18(2):127–136. https://doi.org/10.1037/gpr0000007 CrossRefGoogle Scholar
  6. Diehl JJ, Schmitt LM, Villano M, et al., 2012. The clinical use of robots for individuals with autism spectrum disorders: a critical review. Res Autism Spectr Disord, 6(1):249–262. https://doi.org/10.1016/j.rasd.2011.05.006 CrossRefGoogle Scholar
  7. English BA, Coates A, Howard A, 2017. Recognition of gestural behaviors expressed by humanoid robotic platforms for teaching affect recognition to children with autism—a healthy subjects pilot study. In: Kheddar A, Yoshida E, Ge SS, et al. (Eds.), Social Robotics. Springer, Cham. https://doi.org/10.1007/978-3-319-70022-9_56 Google Scholar
  8. Esteban PG, Baxter P, Belpaeme T, et al., 2017. How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder. Paladyn J Behav Rob, 8(1):18–38.https://doi.org/10.1515/pjbr-2017-0002 CrossRefGoogle Scholar
  9. Greczek J, Matarić M, 2015. Encouraging user autonomy through robot-mediated intervention. Proc 10th ACM/IEEE Int Conf on Human-Robot Interaction Extended, p.189–190. https://doi.org/10.1145/2701973.2702714
  10. Huijnen CAGJ, Lexis MAS, Jansens R, et al., 2016. Mapping robots to therapy and educational objectives for children with autism spectrum disorder. J Autism Dev Disord, 46(6):2100–2114. https://doi.org/10.1007/s10803-016-2740-6 CrossRefGoogle Scholar
  11. Huijnen CAGJ, Lexis MAS, Jansens R, et al., 2017. How to implement robots in interventions for children with autism? A co-creation study involving people with autism, parents and professionals. J Autism Dev Disord, 47(10): 3079–3096. https://doi.org/10.1007/s10803-017-3235-9 CrossRefGoogle Scholar
  12. Lee B, Xu J, Howard A, 2017. Does appearance matter? Validating engagement in therapy protocols with socially interactive humanoid robots. Proc IEEE Symp Series on Computational Intelligence, p.1–6. https://doi.org/10.1109/SSCI.2017.8285303
  13. Matarić MJ, 2017. Socially assistive robotics: human augmentation versus automation. Sci Rob, 2(4), Article eaam5410. https://doi.org/10.1126/scirobotics.aam5410
  14. Munir KM, Lavelle TA, Helm DT, et al., 2016. Autism: a global framework for action. https://doi.org/www.wish.org.qa/summits/wish-2016/forum-reports/
  15. Pennisi P, Tonacci A, Tartarisco G, et al., 2016. Autism and social robotics: a systematic review. Autism Res, 9(2): 165–183. https://doi.org/10.1002/aur.1527 CrossRefGoogle Scholar
  16. Robins B, Dautenhahn K, 2014. Tactile interactions with a humanoid robot: novel play scenario implementations with children with autism. Int J Soc Rob, 6(3):397–415. https://doi.org/10.1007/s12369-014-0228-0 CrossRefGoogle Scholar
  17. Scassellati B, Admoni H, Matarić M, 2012. Robots for use in autism research. Ann Rev Biom Eng, 14:275–294. https://doi.org/10.1146/annurev-bioeng-071811-150036 CrossRefGoogle Scholar
  18. Simut R, van de Perre G, Costescu C, et al., 2016. Probogotchi: a novel edutainment device as a bridge for interaction between a child with ASD and the typically developed sibling. J Evid Based Psychot, 16(1):91–112.Google Scholar
  19. Sun X, Allison C, Matthews FE, et al., 2013. Prevalence of autism in mainland China, Hong Kong and Taiwan: a systematic review and meta-analysis. Mol Autism, 4(1), Article 7. https://doi.org/10.1186/2040-2392-4-7
  20. Wainer J, Dautenhahn K, Robins B, et al., 2014. A pilot study with a novel setup for collaborative play of the humanoid robot KASPAR with children with autism. Int J Soc Rob, 6(1):45–65. https://doi.org/10.1007/s12369-013-0195-x CrossRefGoogle Scholar
  21. Wang S, Jiang M, Duchesne XM, et al., 2015. A typical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron, 88(3):604–616. https://doi.org/10.1016/j.neuron.2015.09.042 CrossRefGoogle Scholar
  22. WUCAILU ASD Research Institute, 2017. Report on the Industry Development of Autism Education and Rehabilitation in China (II). Huaxia Publishing House, Beijing, China (in Chinese).Google Scholar
  23. Zheng ZW, 2017. The Situation of the Life and the Learning of Two Million Autistic Children Became Severe. Modern Education News, Mar. 31, 2017 (in Chinese).Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Guo-bin Wan
    • 1
  • Fu-hao Deng
    • 2
  • Zi-jian Jiang
    • 2
  • Sheng-zhao Lin
    • 2
  • Cheng-lian Zhao
    • 2
  • Bo-xun Liu
    • 2
  • Gong Chen
    • 2
  • Shen-hong Chen
    • 3
  • Xiao-hong Cai
    • 4
  • Hao-bo Wang
    • 5
  • Li-ping Li
    • 6
  • Ting Yan
    • 7
  • Jia-ming Zhang
    • 2
    Email author
  1. 1.Shenzhen Maternal & Child Healthcare HospitalShenzhenChina
  2. 2.Institute of Robotics and Intelligent ManufacturingThe Chinese University of Hong Kong (Shenzhen)ShenzhenChina
  3. 3.School of Mechanical EngineeringZhejiang UniversityHangzhouChina
  4. 4.Institute of Information Engineering & TechnologyHuaqiao UniversityXiamenChina
  5. 5.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina
  6. 6.School of Mechanical and Electrical EngineeringBeijing Information Science and Technology UniversityBeijingChina
  7. 7.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

Personalised recommendations