Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Building Living Lab for Acquiring Behavioral Data for Early Screening of Developmental Disorders

  • Kim, Jung-Jun (Korea Institute of Robotics&Technology Convergence) ;
  • Kwon, Yong-Seop (Korea Institute of Robotics&Technology Convergence) ;
  • Kim, Min-Gyu (Korea Institute of Robotics&Technology Convergence) ;
  • Kim, Eun-Soo (Korea Institute of Robotics&Technology Convergence) ;
  • Kim, Kyung-Ho (Korea Institute of Robotics&Technology Convergence) ;
  • Sohn, Dong-Seop (Korea Institute of Robotics&Technology Convergence)
  • Received : 2020.06.25
  • Accepted : 2020.07.28
  • Published : 2020.08.31
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Abstract

Developmental disorders are impairments of brain and/or central nervous system and refer to a disorder of brain function that affects languages, communication skills, perception, sociality and so on. In diagnosis of developmental disorders, behavioral response such as expressing emotions in proper situation is one of observable indicators that tells whether or not individual has the disorders. However, diagnosis by observation can allow subjective evaluation that leads erroneous conclusion. This research presents the technological environment and data acquisition system for AI based screening of autism disorder. The environment was built considering activities for two screening protocols, namely Autism Diagnostic Observation Schedule (ADOS) and Behavior Development Screening for Toddler (BeDevel). The activities between therapist and baby during the screening are fully recorded. The proposed software in this research was designed to support recording, monitoring and data tagging for learning AI algorithms.

발달장애는 영유아 기부터 시작하는 뇌 신경계 발달장애들의 집합으로 언어 및 의사소통, 인지력, 사회성 등의 측면에서 이루어져야 할 발달이 심하게 지체되거나 성취되지 않은 장애를 의미한다. 이러한 발달장애 진단에는 아동의 얼굴 표정과 같은 감정표현의 의미와 맥락 등 비언어적 반응에 대한 관찰로 이루어진다. 이를 사람이 측정기에는 상당히 주관적인 판단이 개입하게 되어 객관적인 기술이 필요하다. 따라서 본 연구에서는 영유아/아동의 언어, 비언어적 행동 반응을 관찰하는 ADOS(Autism Diagnostic Observation Schedule)와 BeDevel(Behavior Development Screening for Toddler) 검사에서 검사자와 피검사자간의 상호작용이 녹화된 영상을 리빙랩 환경에서 획득하여 인공지능 기반의 비정상적/상동적 행동 인지 기술 개발에 필요한 영상 및 음성 데이터 확보를 목표로 한다.

Keywords

References

  1. Zeanah, Charles H, et al. "Practice parameter for the assessment and treatment of children and adolescents with reactive attachment disorder and disinhibited social engagement disorder." Journal of the Ameracan Academy of Child & Adolescent Psychiatry, Vol.55, No.11, pp.990-1003, 2016. https://doi.org/10.1016/j.jaac.2016.08.004
  2. Wong, Connie, et al. "Evidence-based practices for children, youth, and young adults with autism spectrum disorder: A comprehensive review." Journal of autism and developmental disorders, Vol.45, No.7, pp.1951-1966, 2015. https://doi.org/10.1007/s10803-014-2351-z
  3. Lord, C., et al. "ADOS-2." Escala de Observacion para el Diagnostico del Autismo-2. Manual (T. Luque, adaptadora). Madrid: TEA Ediciones, 2015.
  4. Lord, Catherine, Michael Rutter, and Ann Le Couteur. "Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders." Journal of autism and developmental disorders, Vol.24, No.5, (1994): pp.659-685. https://doi.org/10.1007/BF02172145
  5. Schopler, Eric, Robert Jay Reichler, and Barbara Rochen Renner. The childhood autism rating scale (CARS). Los Angeles: WPS, 2010.
  6. Kim, T. R., and R. G. Park. "Korean version of childhood autism rating scale." Seoul: Special Education (1995).
  7. Constantino, John N., and Christian P. Gruber. Social responsiv eness scale: SRS-2. Torrance, CA: Western Psychological Services, 2012.
  8. Bong, Guiyoung, et al. "The Feasibility and Validity of Autism Spectrum Disorder Screening Instrument: Behavior Development Screening for Toddlers (BeDevel)-A Pilot Study." Autism Research Vol.12, No.7, pp.1112-1128 2019. https://doi.org/10.1002/aur.2117
  9. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444. https://doi.org/10.1038/nature14539
  10. Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. AFEW-VA database for valence and arousal estimation in-the-wild. Image and Vision Computing, 65, 23-36 2017. https://doi.org/10.1016/j.imavis.2017.02.001
  11. KE, Qiuhong, et al. A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3288-3297 2017.
  12. MARINOIU, Elisabeta, et al. "3d human sensing, action and emotion recognition in robot assisted therapy of children with autism." In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2158-2167 2018.
  13. SHEN, J., et al. Autism data goes big: A publicly- accessible multi-modal database of child interactions for behavioural and machine learning research. In: International Society for Autism Research Annual Meeting. 2018.
  14. Nick Veitch (February 2011). "Video tools. MKVToolNix" (PDF). Linux Format. Future plc (141): 77. ISSN 1470-4234.