Skip to main content

Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19

  • Conference paper
  • First Online:
Brain Informatics (BI 2020)

Abstract

Alzheimer patient’s routine care at the onset of a catastrophe like coronavirus disease 2019 (COVID-19) pandemic is interrupted as healthcare is providing special attention to the patient having severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) or COVID-19 infection. In order to decrease the spread of the disease, government has shut down regular services at the hospital, and advised all vulnerable people to stay at home and maintain social distance (of 3 fts) which hampered the routine care and rehabilitation therapy of elderly patient having a chronic disease like Alzheimer. On the other hand, the artificial intelligence (AI)-based internet of healthcare things allows clinicians to monitor physiological conditions of patients in real-time and machine learning models can able to detect any anomaly in the patient’s condition. Besides, the advancement in Information and Communication Technology enable us to provide special distance care (such as medication and therapy) by dedicated medical teams or special therapists. This paper discusses the effect of COVID-19 on patient care of Alzheimer’s Disease (AD) and how AI-based IoT can help special care of AD patients at home. Finally, we have outlined some recommendations for Family and Caregiver, Volunteer and Social Care which will help to develop the Government policy.

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

Access this chapter

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

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Alzheimer’s Association: Alzheimer’s and dementia (1980). https://alz.org/alzheimer_s_dementia. Accessed 10 June 2020

  3. Alzheimers.net: what are the 7 stages of alzheimer’s disease? (1996). https://www.alzheimers.net/stages-of-alzheimers-disease/. Accessed 10 June 2020

  4. Amwell: About Amwell, September 2014. https://business.amwell.com/about-us/. Library Catalog: business.amwell.com

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

    Google Scholar 

  6. Bernell, S., Howard, S.W.: Use your words carefully: what is a chronic disease? Front. Public Health 4, 159 (2016)

    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. Bornstein, S.R., et al.: Practical recommendations for the management of diabetes in patients with COVID-19. Lancet: Diabet. Endocrinol. 8(6), 546–550 (2020)

    Google Scholar 

  9. Brightfocus: Coronavirus and alzheimer’s disease (2020). https://www.brightfocus.org/alzheimers-disease/article/covid-19-and-alzheimers-disease. Library Catalog: www.brightfocus.org

  10. British Geriatrics Society

    Google Scholar 

  11. CDC: What is alzheimer’s disease?|CDC (1946). https://www.cdc.gov/aging/aginginfo/alzheimers.htm. Accessed 14 June 2020

  12. CDC: About chronic diseases|CDC (1947). https://www.cdc.gov/chronicdisease/about/index.htm. Accessed 10 June 2020

  13. ESC Press Office: New - ESC guidance for the diagnosis and management of heart disease during COVID-19 (2020). https://bit.ly/2XYh6U8. Accessed 10 June 2020

  14. Farooq, A., Anwar, S., Awais, M., Alnowami, M.: Artificial intelligence based smart diagnosis of alzheimer’s disease and mild cognitive impairment. In: 2017 ISC2, pp. 1–4. IEEE (2017)

    Google Scholar 

  15. Fisher, C.K., Smith, A.M., Walsh, J.R.: Machine learning for comprehensive forecasting of alzheimer’s disease progression. Sci. Rep. 9(1), 1–14 (2019)

    Article  Google Scholar 

  16. Grassi, M., Perna, G., Caldirola, D., Schruers, K., Duara, R., Loewenstein, D.A.: A clinically-translatable machine learning algorithm for the prediction of alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. J. Alzheimers Dis. 61(4), 1555–1573 (2018)

    Article  Google Scholar 

  17. 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 

  18. Kretchy, I.A., Asiedu-Danso, M., Kretchy, J.P.: Medication management and adherence during the COVID-19 pandemic: perspectives and experiences from low-and middle-income countries. Research in Social & Administrative Pharmacy (2020)

    Google Scholar 

  19. Kuo, C.L., et al.: APOE e4 Genotype Predicts Severe COVID-19 in the UK Biobank Community Cohort. J. Gerontol.: A (2020)

    Google Scholar 

  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., 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)

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

    Article  Google Scholar 

  23. NHS: Is there a cure for dementia? (1948). https://www.nhs.uk/conditions/dementia/cure/. Accessed 10 June 2020

  24. 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, pp. 115–125 (2019)

    Google Scholar 

  25. Park, J.H., et al.: Machine learning prediction of incidence of alzheimer’s disease using large-scale administrative health data. NPJ Digit. Med. 3(1), 1–7 (2020)

    Article  Google Scholar 

  26. Paul, M.C., et al.: Low cost and portable patient monitoring system for e-health services in Bangladesh. In: Proceedings of ICCCI, pp. 1–4 (2016)

    Google Scholar 

  27. Rabby, G., et al.: TeKET: a tree-based unsupervised keyphrase extraction technique. Cogn. Comput. (2020). https://doi.org/10.1007/s12559-019-09706-3

    Article  Google Scholar 

  28. 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 

  29. Selkoe, D.J.: Preventing alzheimer’s disease. Science 337(6101), 1488–1492 (2012)

    Article  Google Scholar 

  30. Sumi, A.I., Zohora, M.F., Mahjabeen, M., Faria, T.J., Mahmud, M., Kaiser, M.S.: fASSERT: a fuzzy assistive system for children with autism using Internet of Things. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 403–412. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_38

    Chapter  Google Scholar 

  31. Taheri, S., et al.: Managing diabetes in Qatar during the COVID-19 pandemic. Lancet: Diabet. Endocrinol. 6(6), 473–474 (2020)

    Google Scholar 

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

    Google Scholar 

  33. Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. Int. J. Ambient Comput. Intell. 1(1), 15–26 (2009)

    Article  Google Scholar 

  34. Wertner, A., Czech, P., Pammer-Schindler, V.: An open labelled dataset for mobile phone sensing based fall detection. In: EAI ICMUSCNS. ACM, August 2015

    Google Scholar 

  35. WHO: Coronavirus (1947). https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 10 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

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

Jesmin, S., Kaiser, M.S., Mahmud, M. (2020). Artificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19. 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_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59277-6_24

  • 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