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
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)
American Psychiatric Association: DSM-5: manual diagnóstico e estatístico de transtornos mentais. Artmed Editora (2014)
Barsoum: Fer+ (face expression recognition plus dataset) (2017). https://github.com/Microsoft/FERPlus
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
Bates, E., Benigni, L., Bretherton, I., Camaioni, L., Volterra, V.: The Emergence of Symbols: Cognition and Communication in Infancy. Academic Press, New York (1979)
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
Elias, N.C.: Teorias comportamentais sobre a etiologia do autismo e uma nova proposta. UEL (2019)
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
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
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
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
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
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
Mordvintsev, A., Abid, K.: Opencv-python tutorials documentation (2014). https://media.readthedocs.org/pdf/opencv-python-tutroals/latest/opencv-python-tutroals.pdf
Mundy, P., Delgado, C., Block, J., Venezia, M., Hogan, A., Seibert, J.: Early Social Communication Scales (ESCS). University of Miami, Coral Gables (2003)
Pavlov, N.: User interface for people with autism spectrum disorders. J. Softw. Eng. Appl. (2014). https://doi.org/10.4236/jsea.2014.72014
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
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
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)
Valentim, N.A.: Experiment data (2022). https://l1nk.dev/experimentdata. Accessed Oct 2022
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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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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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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