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The Artificial Intelligence as a Technological Resource in the Application of Tasks for the Development of Joint Attention in Children with Autism

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14195))

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Abstract

People with autism spectrum disorder (ASD) may present, in addition to deficits in communication, social interaction and patterns of restricted and repetitive behaviors, also present a deficit in joint attention (JA), which refers to the response repertoire of following and/or directing an adult’s visual attention to objects or events in the environment. By having a strong relationship with the learning process, joint attention deficits can compromise a person’s learning process. In this way, the use of technology can help in the development of abilities in people with autism, such as, for example, improving joint attention, communication and social skills. In this context, the general objective of the work proposal was to develop a computational approach for intervention that allows the interaction of the student with autism, with 4 and 5 years old, with deficit in joint attention and social-communicative difficulties. Artificial intelligence (AI) techniques were used to model the most appropriate sequence and level of complexity of exercises for each child. AI resources were used with the intention of providing an intelligent environment to guide the child, dynamically and adaptively, in order to promote stimuli and adequate personalization of the process. In this way, it is intended to contribute significantly to the advancement of the state of the art regarding the production of computational technologies for people with ASD.

Supported by Research Support Foundation of the State of Minas Gerais (FAPEMIG) - UNIVERSAL DEMAND Process: APQ-00837-21.

This research has an opinion embodied by the Research Ethics Committee number 5.273.182, with CAAE 54880921.7.0000.5152, the Proposing Institution being the Faculty of Computing of the Federal University of Uberlândia.

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References

  1. de Almeida, L.G.S.: Padrões de Projeto de Análise para Desenvolvimento de Software do Domínio do Transtorno do Espectro Autista (TEA). Master’s thesis, Universidade Federal Fluminense (2021)

    Google Scholar 

  2. American Psychiatric Association: DSM-5: manual diagnóstico e estatístico de transtornos mentais. Artmed Editora (2014)

    Google Scholar 

  3. Barsoum: Fer+ (face expression recognition plus dataset) (2017). https://github.com/Microsoft/FERPlus

  4. Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z.: Training deep networks for facial expression recognition with crowd-sourced label distribution. In: ACM International Conference on Multimodal Interaction, pp. 279–283 (2016). https://doi.org/10.1145/2993148.2993165

  5. Bates, E., Benigni, L., Bretherton, I., Camaioni, L., Volterra, V.: The Emergence of Symbols: Cognition and Communication in Infancy. Academic Press, New York (1979)

    Google Scholar 

  6. Cardon, T.A., Wilcox, M.J., Campbell, P.H.: Caregiver perspectives about assistive technology use with their young children with autism spectrum disorders. Infants Young Child. 24(2), 153–173 (2011). https://doi.org/10.1097/IYC.0b013e31820eae40

    Article  Google Scholar 

  7. Elias, N.C.: Teorias comportamentais sobre a etiologia do autismo e uma nova proposta. UEL (2019)

    Google Scholar 

  8. Gera, D., Balasubramanian, S.: Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition. Pattern Recogn. Lett. 145, 58–66 (2021). https://doi.org/10.1016/j.patrec.2021.01.029

    Article  Google Scholar 

  9. Ghahramani, Z.: Unsupervised learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML 2003. LNCS (LNAI), vol. 3176, pp. 72–112. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28650-9_5

    Chapter  MATH  Google Scholar 

  10. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)

    Google Scholar 

  11. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia. Association for Computing Machinery (2014). https://doi.org/10.1145/2647868.2654889

  12. Juárez-Ramírez, R., Navarro-Almanza, R., Gomez-Tagle, Y., Licea, G., Huertas, C., Quinto, G.: Orchestrating an adaptive intelligent tutoring system: towards integrating the user profile for learning improvement. Procedia. Soc. Behav. Sci. 106, 1986–1999 (2013). https://doi.org/10.1016/j.sbspro.2013.12.227

    Article  Google Scholar 

  13. Chinea Manrique de Lara, A., Jiménez de Espinoza, C., González-Mora, J.: A fast automated diagnosis system for autism spectrum disorders based on eye tracking technology (2016). https://doi.org/10.13140/RG.2.2.32220.28809

  14. Mordvintsev, A., Abid, K.: Opencv-python tutorials documentation (2014). https://media.readthedocs.org/pdf/opencv-python-tutroals/latest/opencv-python-tutroals.pdf

  15. Mundy, P., Delgado, C., Block, J., Venezia, M., Hogan, A., Seibert, J.: Early Social Communication Scales (ESCS). University of Miami, Coral Gables (2003)

    Google Scholar 

  16. Pavlov, N.: User interface for people with autism spectrum disorders. J. Softw. Eng. Appl. (2014). https://doi.org/10.4236/jsea.2014.72014

    Article  Google Scholar 

  17. Pimenta, T.: Transtorno do espectro autista ou autismo: causas e tratamento (2018). https://www.vittude.com/blog/transtorno-do-espectro-autista-ou-autismo/. Accessed May 2019

  18. Sherkatghanad, Z., et al.: Automated detection of autism spectrum disorder using a convolutional neural network. Front. Neurosci. 13 (2019). https://doi.org/10.3389/fnins.2019.01325

  19. Tenório, M., Vasconcelos, N.: Autismo: a tecnologia como ferramenta assistiva ao processo de ensino e aprendizagem de uma criança dentro do espectro. CINTEDI-Práticas pedagógicas direitos humanos e interculturalidade (2015)

    Google Scholar 

  20. Valentim, N.A.: Experiment data (2022). https://l1nk.dev/experimentdata. Accessed Oct 2022

  21. Vijayan, A., Janmasree, S., Keerthana, C., Syla, L.B.: A framework for intelligent learning assistant platform based on cognitive computing for children with autism spectrum disorder. In: International CET Conference on Control, Communication, and Computing, pp. 361–365 (2018). https://doi.org/10.1109/CETIC4.2018.8530940

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Acknowledgement

I offer my sincerest gratitude to my right arm Research Support Foundation of the State of Minas Gerais (FAPEMIG) - UNIVERSAL DEMAND Process: APQ-00837-21.

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Correspondence to Nathália Assis Valentim .

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Valentim, N.A., Dorça, F.A., Asnis, V.P., Elias, N.C. (2023). The Artificial Intelligence as a Technological Resource in the Application of Tasks for the Development of Joint Attention in Children with Autism. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14195. Springer, Cham. https://doi.org/10.1007/978-3-031-45368-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-45368-7_20

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