Advertisement

Ontological User Modeling for Ambient Assisted Living Service Personalization

  • Maurício Fontana de Vargas
  • Carlos Eduardo Pereira
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 523)

Abstract

Given that the population is aging, it is crucial to develop technologies which will not only help the elderly to age in place, but also live in place with independent and healthy lifestyle. Ambient Assisted Living (AAL) environments can help the elderly and people with functional diversity by anticipating their needs in specific situations and acting proactively in order to properly assist them in performing their activities of daily living (ADLs). Since the users needs tend to be very diverse in regard to functioning and disability levels, it is crucial to have personalized services capable of providing tailored assistance to a user based on their unique preferences, requirements, and desires. This paper introduces the ontology named AATUM (Ambient Assistive Technology User Model), to be adopted by systems whose goal is to enhance user quality of life within ALL environments through service personalization. Its main feature is the use of The International Classification of Functioning, Disability and Health (ICF) to model the user’s functioning and disability levels in a consistent and internationally comparable way. The use of the proposed ontology is illustrated through its application in two different case studies.

Keywords

Ontology Context-aware Functioning User centered World Health Organization 

Notes

Acknowledgment

This research work has been funded by the CAPES PROCAD project (071/2013), whose support is gratefully acknowledged.

References

  1. 1.
    Bhowmick, P.K., Sarkar, S., Basu, A.: Ontology based user modeling for personalized information access. IJCSA 7(1), 1–22 (2010)Google Scholar
  2. 2.
    Centers for Disease Control and Prevention (CDC): The State of Aging and Health in America 2013. US Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta (2013)Google Scholar
  3. 3.
    Fredrich, C., Kuijs, H., Reich, C.: An ontology for user profile modelling in the field of ambient assisted living. In: Sixth International Conferences on Advanced Service Computing, SERVICE COMPUTATION 2014, pp. 24–31 (2014)Google Scholar
  4. 4.
    Hatala, M., Wakkary, R.: Ontology-based user modeling in an augmented audio reality system for museums. User Model. User-Adap. Inter. 15(3–4), 339–380 (2005)CrossRefGoogle Scholar
  5. 5.
    Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo – the general user model ontology. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 428–432. Springer, Heidelberg (2005).  https://doi.org/10.1007/11527886_58CrossRefGoogle Scholar
  6. 6.
    Jiang, X., Tan, A.H.: Learning and inferencing in user ontology for personalized semantic web search. Inf. Sci. 179(16), 2794–2808 (2009)CrossRefGoogle Scholar
  7. 7.
    Kadouche, R., Mokhtari, M., Giroux, S., Abdulrazak, B.: Personalization in smart homes for disabled people. In: Second International Conference on Future Generation Communication and Networking, FGCN 2008, vol. 2, pp. 411–415. IEEE (2008)Google Scholar
  8. 8.
    Lutz, W., Sanderson, W., Scherbov, S.: The coming acceleration of global population ageing. Nature 451(7179), 716–719 (2008)CrossRefGoogle Scholar
  9. 9.
    Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: a guide to creating your first ontology (2001)Google Scholar
  10. 10.
    World Health Organization: International classification of functioning, disability and health: ICF. World Health Organization (2001)Google Scholar
  11. 11.
    World Health Organization: International classification of diseases (ICD) (2012). http://www.who.int/classifications/icd/en/. Retrieved June 2015
  12. 12.
    Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based user modeling for knowledge management systems. In: Brusilovsky, P., Corbett, A., de Rosis, F. (eds.) UM 2003. LNCS (LNAI), vol. 2702, pp. 213–217. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-44963-9_29CrossRefzbMATHGoogle Scholar
  13. 13.
    Rusu, L., Cramariuc, B.: A conceptual approach for innovative home care solution. J. Appl. Comput. Sci. Math. 17(17), 22–26 (2014)Google Scholar
  14. 14.
    de Ruyter, B., Zwartkruis-Pelgrim, E., Aarts, E.: Ambient assisted living research in the carelab. GeroPsych: J. Gerontopsychol. Geriatr. Psychiatr. 23(2), 115 (2010)CrossRefGoogle Scholar
  15. 15.
    Skillen, K.-L., Chen, L., Nugent, C.D., Donnelly, M.P., Burns, W., Solheim, I.: Ontological user profile modeling for context-aware application personalization. In: Bravo, J., López-de-Ipiña, D., Moya, F. (eds.) UCAmI 2012. LNCS, vol. 7656, pp. 261–268. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35377-2_36CrossRefGoogle Scholar
  16. 16.
    Sutterer, M., Droegehorn, O., David, K.: UPOS: User profile ontology with situation-dependent preferences support. In: First International Conference on Advances in Computer-Human Interaction, pp. 230–235. IEEE (2008)Google Scholar
  17. 17.
    Üstün, T.B.: Measuring Health and Disability: Manual for WHO Disability Assessment Schedule WHODAS 2.0. World Health Organization, Geneva (2010)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Maurício Fontana de Vargas
    • 1
  • Carlos Eduardo Pereira
    • 1
  1. 1.Automation Engineering DepartmentFederal University of Rio Grande do SulPorto AlegreBrazil

Personalised recommendations