Automatic Identification of Behavior Patterns in Mild Cognitive Impairments and Alzheimer’s Disease Based on Activities of Daily Living

  • Belkacem ChikhaouiEmail author
  • Maxime Lussier
  • Mathieu Gagnon
  • Hélène Pigot
  • Sylvain Giroux
  • Nathalie Bier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)


The growing number of older adults worldwide places high pressure on identifying dementia at its earliest stages so that early management and intervention strategies could be planned. In this study, we proposed a machine learning based method for automatic identification of behavioral patterns of people with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) through the analysis of data related to their activities of daily living (ADL) collected in two smart home environments. Our method employs first a feature selection technique to extract relevant features for classification and reduce the dimensionality of the data. Then, the output of the feature selection is fed into a random forest classifier for classification. We recruited three groups of participants in our study: healthy older adults, older adults with mild cognitive impairment and older adults with Alzheimer’s disease. We conducted extensive experiments to validate our proposed method. We experimentally showed that our method outperforms state-of-the-art machine learning algorithms.


  1. 1.
    Akl, A., Chikhaoui, B., Mattek, N., Kaye, J., Austin, D., Mihailidis, A.: Clustering home activity distributions for automatic detection of mild cognitive impairment in older adults. J. Ambient. Intell. Smart Environ. 8(4), 437–451 (2016)CrossRefGoogle Scholar
  2. 2.
    Fabrigoule, C., Helmer, C., Rouch, I., Dartigues, J.F., Barberger-Gateau, P.: Functional impairment in instrumental activities of daily living: an early clinical sign of dementia? J. Am. Geriatr. Soc. 47(4), 456–462 (1999)CrossRefGoogle Scholar
  3. 3.
    Rainville, C., Letenneur, L., Dartigues, J.-F., Barberger-Gateau, P.: A hierarchical model of domains of disablement in the elderly: a longitudinal approach. Disabil. Rehabil. 22(7), 308–317 (2000)CrossRefGoogle Scholar
  4. 4.
    Letenneur, L., Barberger-Gateau, P., Dartigues, J.F.: Four instrumental activities of daily living score as a predictor of one-year incident dementia. Age Ageing 22(6), 457–463 (1993)CrossRefGoogle Scholar
  5. 5.
    Bottari, C., Dassa, C., Rainville, C., Dutil, E.: A generalizability study of the instrumental activities of daily living profile. Arch. Phys. Med. Rehabil. 91(5), 734–42 (2010)CrossRefGoogle Scholar
  6. 6.
    Dutil, E., Dassa, C., Bottari, C., Rainville, C.: Choosing the most appropriate environment to evaluate independence in everyday activities: home or clinic? Aust. Occup. Ther. J. 53, 98–106 (2006)CrossRefGoogle Scholar
  7. 7.
    Chikhaoui, B., Gouineau, F.: Towards automatic feature extraction for activity recognition from wearable sensors: a deep learning approach. In: IEEE ICDM Workshops, pp. 693–702 (2017)Google Scholar
  8. 8.
    Dawadi, P., Cook, D.J., Schmitter-Edgecombe, M.: Analyzing activity behavior and movement in a naturalistic environment using smart home techniques. IEEE J. Biomed. Health Inform. 19(6), 1882–92 (2015)CrossRefGoogle Scholar
  9. 9.
    Dawadi, P.N., Cook, D.J., Schmitter-Edgecombe, M., Parsey, C.: Automated assessment of cognitive health using smart home technologies. Technol. Health Care 21(4), 323–343 (2013)Google Scholar
  10. 10.
    Díaz-Uriarte, R., de Andrés, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinform. 7(1), 3 (2006)CrossRefGoogle Scholar
  11. 11.
    Dutil, E., Forget, A., Vanier, M., Gaudreault, C.: Development of the adl profile. Occup. Ther. Health Care 7(1), 7–22 (1990)Google Scholar
  12. 12.
    Pérès, K., et al.: Natural history of decline in instrumental activities of daily living performance over the 10 years preceding the clinical diagnosis of dementia: A prospective population-based study. J. Am. Geriatr. Soc. 56(1), 37–44 (2008)CrossRefGoogle Scholar
  13. 13.
    Winblad, B., et al.: Mild cognitive impairment-beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. J. Intern. Med. 256(3), 240–246 (2004)CrossRefGoogle Scholar
  14. 14.
    Koppel, J., Keehlisen, L., Christen, E., Dreses-Werringloer, U., Conejero-Goldberg, C., Goldberg, T.E.: Performance-based measures of everyday function in mild cognitive impairment. Am. J. Geriatr. Psychiatry 167(7), 845–853 (2010)CrossRefGoogle Scholar
  15. 15.
    Byerly, L.K., Vanderhill, S., Lambe, S., Wong, S., Ozonoff, A., Jefferson, A.L., et al.: Characterization of activities of daily living in individuals with mild cognitive impairment. Am. J. Geriatr. Psychiatry 16(5), 375–383 (2008)CrossRefGoogle Scholar
  16. 16.
    Jekel, K., Damian, M., Storf, H., Hausner, L., Frolich, L.: Development of a proxy-free objective assessment tool of instrumental activities of daily living in mild cognitive impairment using smart home technologies. J. Alzheimers Dis. 52(2), 509–517 (2016)CrossRefGoogle Scholar
  17. 17.
    Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J.L., Chertkow, H., Nasreddine, Z.S., Phillips, N.A.: The montreal cognitive assessment, moca: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53(4), 695–699 (2005)CrossRefGoogle Scholar
  18. 18.
    Petersen, R.C.: Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256(3), 183–194 (2004)CrossRefGoogle Scholar
  19. 19.
    Seelye, A.M., Schmitter-Edgecombe, M., Cook, D.J., Crandall, A.: Naturalistic assessment of everyday activities and prompting technologies in mild cognitive impairment. J. Int. Neuropsychol. Soc. 19(4), 442–452 (2013)CrossRefGoogle Scholar
  20. 20.
    Shallice, T., Burgess, P.W.: Deficits in strategy application following frontal lobe damage in man. Brain 114(2), 727–741 (1991)CrossRefGoogle Scholar
  21. 21.
    Sikkes, S.A., et al.: Do instrumental activities of daily living predict dementia at 1- and 2-year follow-up? Findings from the development of screening guidelines and diagnostic criteria for predementia alzheimer’s disease study. J. Am. Geriatr. Soc. 59(12), 2273–2281 (2011)CrossRefGoogle Scholar
  22. 22.
    Vehtari, A., Gelman, A., Gabry, J.: Practical bayesian model evaluation using leave-one-out cross-validation and waic. Stat. Comput. 27(5), 1413–1432 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Belkacem Chikhaoui
    • 1
    Email author
  • Maxime Lussier
    • 2
  • Mathieu Gagnon
    • 3
  • Hélène Pigot
    • 3
  • Sylvain Giroux
    • 3
  • Nathalie Bier
    • 2
  1. 1.Department of Science and TechnologyTELUQ UniversityMontrealCanada
  2. 2.École de réadaptation, Faculté de médecineUniversité de MontréalMontrealCanada
  3. 3.Domus LaboratoryUniversity of SherbrookeSherbrookeCanada

Personalised recommendations