skip to main content
10.1145/3389189.3398003acmotherconferencesArticle/Chapter ViewAbstractPublication PagespetraConference Proceedingsconference-collections
research-article

A three-module proposed solution to improve cognitive and social skills of students with attention deficit disorder (ADD) and high functioning autism (HFA): innovative technological advancements for students with neurodevelopmental disorders

Published:30 June 2020Publication History

ABSTRACT

Latest technological developments can provide the tools towards a better future for people with special needs. The present paper focuses on Innovative Technological Interventions for Children with Attention Deficit Disorder (ADD) and High Functioning Autism (HFA) with main objective to improve their cognitive and social skills and allow them to gain maximum benefit from social inclusion within the educational system. It addresses the opportunity to present these students with personalized accessibility options that will minimize the aforementioned disadvantages as opposed to typically developing children. The target group is students of the Primary Years Program and Middle Years Program, which is the most sensitive period for the still developing brain.

Skip Supplemental Material Section

Supplemental Material

a72-manta.mp4

mp4

19.9 MB

References

  1. "DSM-5." [Online]. Available: https://www.psychiatry.org/psychiatrists/practice/dsm. [Accessed: 02-May-2020].Google ScholarGoogle Scholar
  2. A. Miranda, C. Berenguer, B. Roselló, I. Baixauli, and C. Colomer, "Social cognition in children with high-functioning autism spectrum disorder and attention-deficit/hyperactivity disorder, associations with executive functions," Front. Psychol., vol. 8, no. JUN, 2017.Google ScholarGoogle Scholar
  3. J. Y. F. de Lima Antão et al., "Instruments for augmentative and alternative communication for children with autism spectrum disorder: a systematic review.," Clinics (Sao Paulo)., vol. 73, p. e497, 2018.Google ScholarGoogle Scholar
  4. I. Scholl, A. LaRussa, P. Hahlweg, S. Kobrin, and G. Elwyn, "Organizational- and system-level characteristics that influence implementation of shared decision-making and strategies to address them - a scoping review," Implementation Science, vol. 13, no. 1. BioMed Central Ltd., p. 40, 09-Mar-2018.Google ScholarGoogle Scholar
  5. D. Murano, A. A. Lipnevich, K. E. Walton, J. Burrus, J. D. Way, and C. Anguiano-Carrasco, "Measuring social and emotional skills in elementary students: Development of self-report Likert, situational judgment test, and forced choice items," Pers. Individ. Dif., p. 110012, Apr. 2020.Google ScholarGoogle ScholarCross RefCross Ref
  6. H.-L. Chiang et al., "School dysfunction in youth with autistic spectrum disorder in Taiwan: The effect of subtype and ADHD," Autism Res., Feb. 2018.Google ScholarGoogle Scholar
  7. W. J. Chou et al., "Self-reported and parent-reported school bullying in adolescents with high functioning autism spectrum disorder: The roles of autistic social impairment, attention-deficit/hyperactivity and oppositional defiant disorder symptoms," Int. J. Environ. Res. Public Health, vol. 16, no. 7, Apr. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  8. B. Wingfield et al., "A predictive model for paediatric autism screening," Health Informatics J., p. 146045821988782, Mar. 2020.Google ScholarGoogle ScholarCross RefCross Ref
  9. A. Aldiab, H. Chowdhury, A. Kootsookos, F. Alam, and H. Allhibi, "Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia," in Energy Procedia, 2019, vol. 160, pp. 731--737.Google ScholarGoogle ScholarCross RefCross Ref
  10. Y.-L. Tian, T. Kanade, and J. F. Cohn, "Chapter 11. Facial Expression Analysis."Google ScholarGoogle Scholar
  11. N. Samadiani et al., "A review on automatic facial expression recognition systems assisted by multimodal sensor data," Sensors (Switzerland), vol. 19, no. 8. MDPI AG, 02-Apr-2019.Google ScholarGoogle Scholar
  12. Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, "Artificial neural networks enabled by nanophotonics," Ligth: Science and Applications, vol. 8, no. 1. Nature Publishing Group, 01-Dec-2019.Google ScholarGoogle Scholar
  13. "HMMs in Protein Fold Classification. - PubMed - NCBI." [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/28224488. [Accessed: 16-Mar-2020].Google ScholarGoogle Scholar
  14. L. Zhu, K. Ikeda, S. Pang, T. Ban, and A. Sarrafzadeh, "Merging weighted SVMs for parallel incremental learning," Neural Networks, vol. 100, pp. 25--38, Apr. 2018.Google ScholarGoogle ScholarCross RefCross Ref
  15. S. Mallik and Z. Zhao, "Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.," Brief. Bioinform., Jan. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  16. T. Ruffman, R. Then, C. Cheng, and K. Imuta, "Lifespan differences in emotional contagion while watching emotion-eliciting videos," PLoS One, vol. 14, no. 1, p. e0209253, Jan. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  17. "Crowd Sourcing. - PubMed - NCBI." [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/27039640. [Accessed: 02-May-2020].Google ScholarGoogle Scholar
  18. M. C. Chiu and C. H. Tsai, "Design a personalised product service system utilising a multi-agent system," Adv. Eng. Informatics, vol. 43, p. 101036, Jan. 2020.Google ScholarGoogle ScholarCross RefCross Ref
  19. W. Li, C. Feng, K. Yu, and D. Zhao, "MISS-D: A fast and scalable framework of medical image storage service based on distributed file system," Comput. Methods Programs Biomed., vol. 186, p. 105189, Apr. 2020.Google ScholarGoogle ScholarCross RefCross Ref
  20. M. Xu, X. Xiao, Y. Wang, H. Qi, T. P. Jung, and D. Ming, "A Brain-Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli," IEEE Trans. Biomed. Eng., vol. 65, no. 5, pp. 1166--1175, May 2018.Google ScholarGoogle ScholarCross RefCross Ref
  21. "(22) (PDF) The Pluralistic Usability Walk-Through Method." [Online]. Available: https://www.researchgate.net/publication/251670702_The_Pluralistic_Usability_Walk-Through_Method. [Accessed: 02-May-2020].Google ScholarGoogle Scholar
  22. "Methods - Screening for Autism Spectrum Disorder in Young Children - NCBI Bookshelf." [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK349706/. [Accessed: 02-May-2020].Google ScholarGoogle Scholar
  23. S. Marien et al., "A User-Centered design and usability testing of a web-based medication reconciliation application integrated in an eHealth network," Int. J. Med. Inform., vol. 126, pp. 138--146, Jun. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  24. F. Todhunter, "Using concurrent think-aloud and protocol analysis to explore student nurses' social learning information communication technology knowledge and skill development," Nurse Educ. Today, vol. 35, no. 6, pp. 815--822, Jun. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  25. M. Kleynen, A. Moser, F. A. Haarsma, A. J. Beurskens, and S. M. Braun, "Physiotherapists use a great variety of motor learning options in neurological rehabilitation, from which they choose through an iterative process: a retrospective think-aloud study," Disabil. Rehabil., vol. 39, no. 17, pp. 1729--1737, Aug. 2017.Google ScholarGoogle ScholarCross RefCross Ref
  26. H. Cho, D. Powell, A. Pichon, L. M. Kuhns, R. Garofalo, and R. Schnall, "Eye-tracking retrospective think-aloud as a novel approach for a usability evaluation," Int. J. Med. Inform., vol. 129, pp. 366--373, Sep. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  27. S.-L. Hwang-Gu et al., "Symptoms of ADHD Affect Intrasubject Variability in Youths with Autism Spectrum Disorder: An Ex-Gaussian Analysis," J. Clin. Child Adolesc. Psychol., pp. 1--14, May 2018.Google ScholarGoogle Scholar
  28. M. Kulkarni, "Discursive work within weak field mandate events: The case of a conference on assistive technologies for persons with disabilities," in IIMB Management Review, 2018, vol. 30, no. 4, pp. 291--304.Google ScholarGoogle ScholarCross RefCross Ref
  29. M. Zanker, L. Rook, and D. Jannach, "Measuring the impact of online personalisation: Past, present and future," Int. J. Hum. Comput. Stud., vol. 131, pp. 160--168, Nov. 2019.Google ScholarGoogle ScholarCross RefCross Ref
  30. E. Coiera, B. Kocaballi, J. Halamka, and L. Laranjo, "The digital scribe," npj Digit. Med., vol. 1, no. 1, Dec. 2018.Google ScholarGoogle Scholar
  31. D. Li, L. Larsen, Y. Yang, L. Wang, Y. Zhai, and W. C. Sullivan, "Exposure to nature for children with autism spectrum disorder: Benefits, caveats, and barriers," Heal. Place, vol. 55, pp. 71--79, Jan. 2019.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. A three-module proposed solution to improve cognitive and social skills of students with attention deficit disorder (ADD) and high functioning autism (HFA): innovative technological advancements for students with neurodevelopmental disorders

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Other conferences
              PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
              June 2020
              574 pages
              ISBN:9781450377737
              DOI:10.1145/3389189

              Copyright © 2020 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 30 June 2020

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader