Skip to main content
SpringerLink
Log in
Book cover

International Conference on Brain Informatics

BI 2020: Brain Informatics pp 251–262Cite as

A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12241))

Abstract

When the world is suffering from the deadliest consequences of COVID-19, people with autism find themselves in the worst possible situation. The patients of autism lack social skills, and in many cases, show repetitive behavior. Many of them need outside support throughout their life. During the COVID-19 pandemic, as many of the places are in lockdown conditions, it is very tough for them to find help from their doctors and therapists. Suddenly, the caregivers and parents of the ASD patients find themselves in a strange situation. Therefore, we are proposing an artificial intelligence-based system that uses sensor data to monitor the patient’s condition, and based on the emotion and facial expression of the patient, adjusts the learning method through exciting games and tasks. Whenever something goes wrong with the patient’s behavior, the caregivers and the parents are alerted about it. We then presented how this AI-based system can help them during COVID-19 pandemic. This system can help the parents to adjust to the new situation and continue the mental growth of the patients.

Md. H. A. Banna and T. Ghosh—These two authors contributed equally.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Afrin, M., Freeda, S., Elakia, S., Kannan, P.: AI based facial expression recognition for autism children. IJETIE 5(9), 7 (2019)

    Google Scholar 

  2. Afsana, F., Mamun, S., Kaiser, M., Ahmed, M.: Outage capacity analysis of cluster-based forwarding scheme for body area network using nano-electromagnetic communication. In: Proceedings of EICT, pp. 383–388 (2015)

    Google Scholar 

  3. Afsana, F., et al.: An energy conserving routing scheme for wireless body sensor nanonetwork communication. IEEE Access 6, 9186–9200 (2018)

    Article  Google Scholar 

  4. Al Banna, M.H., et al.: Camera model identification using deep CNN and transfer learning approach. In: Proceedings of ICREST, pp. 626–630 (2019)

    Google Scholar 

  5. Ali, H.M., Kaiser, M.S., Mahmud, M.: Application of convolutional neural network in segmenting brain regions from MRI data. In: Liang, P., Goel, V., Shan, C. (eds.) Brain Informatics, pp. 136–146. Cham (2019)

    Google Scholar 

  6. Asif-Ur-Rahman, M., et al.: Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things. IEEE Internet Things J. 6(3), 4049–4062 (2018)

    Article  Google Scholar 

  7. Biswas, S., et al.: Cloud based healthcare application architecture and electronic medical record mining: an integrated approach to improve healthcare system. In: Proceedings of ICCIT, pp. 286–291 (2014)

    Google Scholar 

  8. Courtenay, K., Perera, B.: Covid-19 and people with intellectual disability: impacts of a pandemic. Irish J. Psychol. Med. 1–21 (2020)

    Google Scholar 

  9. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Proceedings of CVPR, pp. 248–255 (2009)

    Google Scholar 

  10. Elsabbagh, M., et al.: Global prevalence of autism and other pervasive developmental disorders. Autism Res. 5(3), 160–179 (2012)

    Article  Google Scholar 

  11. Fabietti, M., et al.: Neural network-based artifact detection in LFP recorded from chronically implanted neural probes. In: Proceedings of IJCNN, pp. 1–8 (2020)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR, pp. 770–778 (2016)

    Google Scholar 

  13. Heni, N., Hamam, H.: Design of emotional educational system mobile games for autistic children. In: Proceedings of ATSIP, pp. 631–637 (2016)

    Google Scholar 

  14. James, W.: Domestic violence reports rise by a third in locked-down London, police say (2020). https://www.reuters.com/article/us-health-coronavirus-britain-violence/domestic-violence-reports-rise-by-a-third-in-locked-down-london-police-say-idUSKCN2262YI. Accessed 6 June 2020

  15. Kaiser, M.S., et al.: Advances in crowd analysis for urban applications through urban event detection. IEEE Trans. Intell. Transp. Syst. 19(10), 3092–3112 (2018)

    Article  Google Scholar 

  16. Khullar, V., et al.: IoT based assistive companion for hypersensitive individuals (ACHI) with autism spectrum disorder. Asian J. Psychiat. 46, 92–102 (2019)

    Article  Google Scholar 

  17. Krysko, K.M., Rutherford, M.: A threat-detection advantage in those with autism spectrum disorders. Brain Cogn. 69(3), 472–480 (2009)

    Article  Google Scholar 

  18. Lucey, P., et al.: The extended Cohn-Kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: Proceedings of CVPR, pp. 94–101 (2010)

    Google Scholar 

  19. Mahmud, M., Kaiser, M.S., Hussain, A.: Deep learning in mining biological data. arXiv:2003.00108 [cs, q-bio, stat] abs/2003.00108, pp. 1–36 (2020)

  20. Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)

    Article  MathSciNet  Google Scholar 

  21. Mahmud, M., et al.: A brain-inspired TMM to assure security in a cloud based IoT framework for neuroscience applications. Cogn. Comput. 10(5), 864–873 (2018)

    Article  Google Scholar 

  22. Miah, Y., et al.: Performance comparison of ml techniques in identifying dementia from open access clinical datasets. In: Proceedings of ICACIn, pp. 69–78 (2020)

    Google Scholar 

  23. Noor, M.B.T., et al.: Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective. In: Liang, P., Goel, V., Shan, C. (eds.) Brain Informatics. LNCS, pp. 115–125. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-37078-7_12

    Chapter  Google Scholar 

  24. Pierre Luc Carrier, A.C.: Challenges in representation learning: facial expression recognition challenge (2013). https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset. Accessed 11 June 2020

  25. Rahman, S., Al Mamun, S., Ahmed, M.U., Kaiser, M.S.: PHY/MAC layer attack detection system using neuro-fuzzy algorithm for IoT network. In: Proceedings of ICEEOT, pp. 2531–2536 (2016)

    Google Scholar 

  26. Smitha, K.G., Vinod, A.P.: Facial emotion recognition system for autistic children: a feasible study based on FPGA implementation. Med. Biol. Eng. Comput. 53(11), 1221–1229 (2015). https://doi.org/10.1007/s11517-015-1346-z

    Article  Google Scholar 

  27. de Sousa Lima, et al.: Could autism spectrum disorders be a risk factor for COVID-19? Med. Hypotheses, 144, 109899 (2020)

    Google Scholar 

  28. Sumi, A.I., et al.: fASSERT: a fuzzy assistive system for children with autism using internet of things. In: Brain Informatics, pp. 403–412 (2018)

    Google Scholar 

  29. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of CVPR, pp. 1–9 (2015)

    Google Scholar 

  30. Szegedy, C., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of AAAI, pp. 4278–4284 (2017)

    Google Scholar 

  31. Tania, M.H., et al.: Assay type detection using advanced machine learning algorithms. In: Proceedings of SKIMA, pp. 1–8 (2019)

    Google Scholar 

  32. Viola, P., Jones, M.: Face detection. IJCV 57, 2 (2004)

    Article  Google Scholar 

  33. Watkins, J., Fabietti, M., Mahmud, M.: Sense: a student performance quantifier using sentiment analysis. In: Proceedings of IJCNN, pp. 1–6 (2020)

    Google Scholar 

  34. Weiss, J.A., Fardella, M.A.: Victimization and perpetration experiences of adults with autism. Front. Psychiat. 9, 203 (2018)

    Article  Google Scholar 

  35. WHO: Autism spectrum disorders (2019). https://www.kaggle.com/jonathanoheix/face-expression-recognition-dataset. Accessed 11 June 2020

  36. Yahaya, S.W., Lotfi, A., Mahmud, M.: A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl. Soft Comput. 83, 105613 (2019)

    Article  Google Scholar 

  37. Yahaya, S.W., et al.: Gesture recognition intermediary robot for abnormality detection in human activities. In: Proceedings of SSCI, pp. 1415–1421 (2019)

    Google Scholar 

  38. Yarımkaya, E., Esentürk, O.K.: Promoting physical activity for children with autism spectrum disorders during coronavirus outbreak: benefits, strategies, and examples. Int. J. Dev. Disabil. 1–6 (2020)

    Google Scholar 

  39. Zohora, M.F., et al.: Forecasting the risk of type ii diabetes using reinforcement learning. In: Proceedings of ICIEV, pp. 1–6 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bangladesh University of Professionals, Dhaka, Bangladesh

    Md. Hasan Al Banna, Tapotosh Ghosh & Kazi Abu Taher

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

  3. Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK

    Mufti Mahmud

Authors
  1. Md. Hasan Al Banna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tapotosh Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kazi Abu Taher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Shamim Kaiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mufti Mahmud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University of Padua, Padua, Italy

    Stefano Vassanelli

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al Banna, M.H., Ghosh, T., Taher, K.A., Kaiser, M.S., Mahmud, M. (2020). A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59277-6_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59276-9

  • Online ISBN: 978-3-030-59277-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation