Advertisement

ADMemento: A Prototype of Activity Reminder and Assessment Tools for Patients with Alzheimer’s Disease

  • Sarah AlhassanEmail author
  • Wafa Alrajhi
  • Amal Alhassan
  • Alreem Almuhrij
Conference paper
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10283)

Abstract

Due to the need to restore the life balance of mild-stage Alzheimer’s disease (AD) patients and their caregivers, prototype of the system presented in this paper, ADMemento, enhances the independency of AD patients and reduces the caregivers’ burden. The proposed mobile application includes an activity reminder that sends notifications regarding daily activities and events to patients with their preferred prompts. By observing AD symptoms, the system utilizes some assessment tools as an aid to determine AD progress and early intervention treatment. These assessments, which include recall rates determined by the activity reminder, pronunciation, common knowledge and family-related information loss indications, are presented to caregivers and therapists. A wearable wristband sensor is used to derive the patient’s stress levels that are triggered due to the life-changing and cognitive impairment that patient is facing. ADMemento applies interface design guidelines that are suitable for aging.

Keywords

Social computing Dementia Alzheimer’s disease Activity reminder AD assessment AD aid 

References

  1. 1.
    Lindmeier, C., Brunier, A.: WHO: number of people over 60 years set to double by 2050; major societal changes required. World Health Organization (2015). goo.gl/qIW8dv. Accessed 13 Oct 2016
  2. 2.
    Thies, W., Bleiler, L.: 2011 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 7, 208–244 (2011). doi: 10.1016/j.jalz.2011.02.004 CrossRefGoogle Scholar
  3. 3.
    Rosenblatt, A.: The art of managing dementia in the elderly. Clevel. Clin. J. Med. (2005). doi: 10.3949/ccjm.72.suppl_3.s3
  4. 4.
    Alzheimer’s Disease Education and Referral Center: National Institute on Aging. www.nia.nih.gov/alzheimers. Accessed 15 Oct 2016
  5. 5.
    Centers for Disease Control and Prevention. www.cdc.gov. Accessed 15 Oct 2016
  6. 6.
    Alzheimer’s Association. www.alz.org. Accessed 20 Oct 2016
  7. 7.
    American Psychological Association: Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychological Association, Arlington (2000)Google Scholar
  8. 8.
    Wherton, J., Monk, A.: Technological opportunities for supporting people with dementia who are living at home. Int. J. Hum.-Comput. Stud. 66, 571–586 (2008). doi: 10.1016/j.ijhcs.2008.03.001 CrossRefGoogle Scholar
  9. 9.
    Alzheimer’s Society. www.alzheimers.org.uk. Accessed 20 Oct 2016
  10. 10.
    Hong Kong Hospital Authority: Dementia. In: Smart Patient Websie (2014). goo.gl/ZTqbWP. Accessed 23 Oct 2016
  11. 11.
    Bergman, H., Arcand, M., Bureau, C., et al.: Relever le défi de la maladie d’Alzheimer et des maladies apparentées Une vision centrée sur la personne, l’humanisme et l’excellence. Gouvernement du Québec (2009)Google Scholar
  12. 12.
    Topo, P.: Technology studies to meet the needs of people with dementia and their caregivers: a literature review. J. Appl. Gerontol. 28, 5–37 (2009). doi: 10.1177/0733464808324019 CrossRefGoogle Scholar
  13. 13.
    Gogia, P., Rastogi, N.: Clinical Alzheimer Rehabilitation. Springer, New York (2009)Google Scholar
  14. 14.
    Eisdorfer, C., Czaja, S., Loewenstein, D., et al.: The effect of a family therapy and technology-based intervention on caregiver depression. The Gerontologist 43, 521–531 (2003). doi: 10.1093/geront/43.4.521 CrossRefGoogle Scholar
  15. 15.
    Mahoney, D., Tarlow, B., Jones, R.: Effects of an automated telephone support system on caregiver burden and anxiety: findings from the REACH for TLC intervention study. The Gerontologist 43, 556–567 (2003). doi: 10.1093/geront/43.4.556 CrossRefGoogle Scholar
  16. 16.
    Warren, S., Yao, J., Barnes, G.: Wearable sensors and component-based design for home health care. In: Proceedings of the Second Joint of 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference on Engineering in Medicine and Biology, pp. 1871–1872. IEEE (2002)Google Scholar
  17. 17.
    Choi, J., Ahmed, B., Gutierrez-Osuna, R.: Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16, 279–286 (2012). doi: 10.1109/titb.2011.2169804 CrossRefGoogle Scholar
  18. 18.
    Taylor, F.: Care managers’ views on assistive technology. J. Dement. Care 13, 32–35 (2016)Google Scholar
  19. 19.
    Oriani, M., Moniz-Cook, E., Binetti, G., et al.: An electronic memory aid to support prospective memory in patients in the early stages of Alzheimer’s disease: a pilot study. Aging Ment. Health 7, 22–27 (2003). doi: 10.1080/1360786021000045863 CrossRefGoogle Scholar
  20. 20.
    Labelle, K., Mihailidis, A.: The use of automated prompting to facilitate handwashing in persons with dementia. Am. J. Occup. Ther. 60, 442–450 (2006). doi: 10.5014/ajot.60.4.442 CrossRefGoogle Scholar
  21. 21.
    Wang, Q., Shin, W., Liu, X., et al.: An open system architecture for assisted living. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006 (2006)Google Scholar
  22. 22.
    How the Mem-x Voice Reminder Works. Pivotell. goo.gl/Gcu0XH. Accessed 27 Sept 2016
  23. 23.
    Morrison, K., Szymkowiak, A., Gregor, P.: Memojog – an interactive memory aid incorporating mobile based technologies. In: Brewster, S., Dunlop, M. (eds.) Mobile HCI 2004. LNCS, vol. 3160, pp. 481–485. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28637-0_61 CrossRefGoogle Scholar
  24. 24.
    Bieber, T., Leung, D. (eds.): Atopic Dermatitis, 1st edn. Marcel Dekker, New York (2002)Google Scholar
  25. 25.
    Lapointe, J., Bouchard, B., Bouchard, J., et al.: Smart homes for people with Alzheimer’s disease: adapting prompting strategies to the patient’s cognitive profile. In: The 5th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2012 (2012)Google Scholar
  26. 26.
    Fraser, K., Meltzer, J., Rudzicz, F.: Linguistic features identify Alzheimer’s disease in narrative speech. J. Alzheimer’s Dis. 49, 407–422 (2015). doi: 10.3233/jad-150520 CrossRefGoogle Scholar
  27. 27.
    Speech API - Speech Recognition. Google Cloud Platform. cloud.google.com/speech/. Accessed 5 Nov 2016
  28. 28.
    Endler, N.: Hassles, health, and happiness. In: Janisse, M. (ed.) Individual Differences, Stress, and Health Psychology, pp. 24–56. Springer, New York (1988)CrossRefGoogle Scholar
  29. 29.
    Pozo, G., Vázquez, I., Ávila, C., et al.: State of the Art-Wearable Sensors (2014)Google Scholar
  30. 30.
    E4 wristband: Empatica. www.empatica.com/e4-wristband. Accessed 24 Nov 2016
  31. 31.
    Gross, T., Gulliksen, J., Kotzé, P. (eds.): INTERACT 2009. HCI, vol. 5727. Springer, Heidelberg (2009)Google Scholar
  32. 32.
    Simola, A.: The Roving Mind: A Modern Approach to Cognitive Enhancement. ST Press, New York (2015)Google Scholar
  33. 33.
    Filippini, D. (ed.): Autonomous Sensor Networks. Springer, Berlin (2013)Google Scholar
  34. 34.
    Palanisamy, K., Murugappan, M., Yaacob, S.: Multiple physiological signal-based human stress identification using non-linear classifiers. Elektronika IR Elektrotechnika (2013). doi: 10.5755/j01.eee.19.7.2232
  35. 35.
    Sharma, N., Gedeon, T.: Artificial neural network classification models for stress in reading. In: Huang, T., Zeng, Z., Li, C., et al. (eds.) Neural Information Processing, pp. 388–395. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  36. 36.
    Haug, C., Kvam, F.: Tablets and elderly users: designing a guidebook. Master’s thesis, University of OSLO Department of informaticsGoogle Scholar
  37. 37.
    Thakur, T., Sethi, D.: A descriptive study to assess the impact of low vision on activities of daily living among elderly people living in selected residential areas of Kurali (Punjab). Imperial J. Interdiscip. Res. 2, 711–717 (2016)Google Scholar
  38. 38.
    Slavíček, T.: Touch screen mobile user interface for seniors. Master’s thesis, Czech Technical University (2014)Google Scholar
  39. 39.
    Echt, K.: Designing web-based health information for older adults: visual consideration and design directives. In: Morrell, R. (ed.) Older Adults, Health Information, and the World Wide Web, pp. 59–86. Lawrence Erlbaum Associates, Mahwah (2002)Google Scholar
  40. 40.
    Galitz, W.: The Essential Guide to User Interface Design, 3rd edn. Wiley, Indianapolis (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sarah Alhassan
    • 1
    Email author
  • Wafa Alrajhi
    • 1
  • Amal Alhassan
    • 2
  • Alreem Almuhrij
    • 1
  1. 1.Computer Science DepartmentAl Imam Muhammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia
  2. 2.Information Technology DepartmentKing Saud UniversityRiyadhSaudi Arabia

Personalised recommendations