Conceptual Modeling for Ambient Assistance

  • Judith Michael
  • Heinrich C. Mayr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8217)


This paper addresses the conceptual modeling of a person’s daily activities, i.e. units of purposeful individual behavior. An integrated set of such models is intended to be used as a knowledge base for supporting that person by an intelligent system when he/she requires so. The work is part of the HBMS1 project, a research project in the field of Ambient Assisted Living: HBMS aims at supporting people with declining memory by action know-how they previously had in order to prolong their ability to live autonomously at home.


Conceptual Behavior Modeling Ambient Assistance Behavioral Support Cognitive Impairments Activity Theory User Context 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arezki, A., Monacelli, E., Alayli, Y.: Ambient Assistance Using Mobile Agents. In: Proc. of the First Int. Conf. on Smart Systems, Devices and Technologies, pp. 89–95 (2012)Google Scholar
  2. 2.
    Steg, H., et al.: Europe Is Facing a Demographic Challenge. In: Ambient Assisted Living Of-fers Solutions. VDI/VDE/IT, Berlin (2006)Google Scholar
  3. 3.
    Pryss, R., Tiedeken, J., Kreher, U., Reichert, M.: Towards Flexible Process Support on Mobile Devices. In: Soffer, P., Proper, E. (eds.) CAiSE Forum 2010. LNBIP, vol. 72, pp. 150–165. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Baumeister, J., Reutelshoefer, J., Puppe, F.: KnowWE: A SemanticWiki for Knowledge Engineering. In: Applied Intelligence, vol. 35(3), pp. 323–344 (2011)Google Scholar
  5. 5.
    Katz, S.: Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the Am. Geriatrics Society (31), 721–727 (1983)Google Scholar
  6. 6.
    Zhou, F., et al.: A Case-Driven Ambient Intelligence System for Elderly in-Home Assistance Applications. Institute of Electrical and Electronics Engineers, New York (2011)Google Scholar
  7. 7.
    Giroux, S., et al.: Pervasive behavior tracking for cognitive assistance. In: Proc. of the Int. Conf. on Pervasive Technologies Related to Assistive Environments. ACM, NY (2008)Google Scholar
  8. 8.
    Griesser, A., Michael, J., Mayr, H.C.: Verhaltensmodellierung und automatisierte Unterstützung im AAL Projekt HBMS. In: Proc. AAL 2012, Berlin (2012)Google Scholar
  9. 9.
    Kofod-Petersen, A., Mikalsen, M.: Context: Representation and Reasoning, Special issue of the Revue d’Intelligence Artificielle on "Applying Context-Management" (2005)Google Scholar
  10. 10.
    Wohed, P., van der Aalst, W.M.P., Dumas, M., ter Hofstede, A.H.M., Russell, N.: Pattern-Based Analysis of the Control-Flow Perspective of UML Activity Diagrams. In: Delcambre, L.M.L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 63–78. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Wohed, P., van der Aalst, W.M.P., Dumas, M., ter Hofstede, A.H.M., Russell, N.: On the Suitability of BPMN for Business Process Modelling. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 161–176. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Karagiannis, D., Grossmann, W., Höfferer, P.: Open Model Initiative: A Feasibility Study. University of Vienna, Dpmt. of Knowledge Engineering (2002),
  13. 13.
    Michael, J., Grießer, A., Strobl, T., Mayr, H.C.: Cognitive Modeling and Support for Ambient Assistance. In: Kop, C. (ed.) UNISON 2012. LNBIP, vol. 137, pp. 96–107. Springer, Heidelberg (2013)Google Scholar
  14. 14.
    Michael, J., Bolshutkin, V., Leitner, S., Mayr, H.C.: Behavior Modeling for Ambient Assistance. In: Proc. Int. Conf. on Management and Service Science (MASS), Shanghai (2012)Google Scholar
  15. 15.
    Shekhovtsov, V., Mayr, H.C.: A Conceptualization of Quality Management Functionality in Cognitive Assistance Systems (submitted for publication)Google Scholar
  16. 16.
    Mayr, H.C., Michael, J.: Control pattern based analysis of HCM-L, a language for cognitive modeling. In: Proc. ICTer 2012, pp. 169–175. IEEE (2012)Google Scholar
  17. 17.
    Silverman, B.G., et al.: Toward A Human Behavior Models Anthology for Synthetic Agent Development. In: Proceedings of the Conference on Computer Generated Forces and Behavioral Representation. SISO (2001)Google Scholar
  18. 18.
    Zacharias, G., MacMillan, J., Van Hemel, S.B. (eds.): Behavioral Modeling and Simulation: From Individuals to Societies. The National Academies Press (2008)Google Scholar
  19. 19.
    Leont’ev, A.N.: Activity, Consciousness, and Personality. Prentice-Hall (1978)Google Scholar
  20. 20.
    Bannon, L., Bødker, S.: Beyond the interface: Encountering artifacts in use. In: Carroll, J. (ed.) Designing Interaction: Psychology at the Human-Computer Interface. Cambridge University Press, Cambridge (1991)Google Scholar
  21. 21.
    Kofod-Petersen, A., Cassens, J.: Using Activity Theory to Model Context Awareness. In: Roth-Berghofer, T.R., Schulz, S., Leake, D.B. (eds.) MRC 2005. LNCS (LNAI), vol. 3946, pp. 1–17. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Clement, J., Ploennigs, J., Kabitzsch, K.: Smart Meter: Detect and Individualize ADLs. In: Proc. AAL 2012, Berlin (2012)Google Scholar
  23. 23.
    Hesse, W., Mayr, H.C.: Modellierung in der Softwaretechnik: eine Bestandsaufnahme. Informatik-Spektrum 31(5), 377–393 (2008)CrossRefGoogle Scholar
  24. 24.
    Allweyer, T.: BPMN 2.0 Introduction to the Standard for Business Process Modeling. BoD – Books on Demand (2009)Google Scholar
  25. 25.
    Kop, C., Mayr, H.C.: Conceptual Predesgin - Bridging the Gap between Requirements and Conceptual Design. In: Proc. ICRE 1998. Colorado Springs (1998)Google Scholar
  26. 26.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7, 39–59 (1994)Google Scholar
  27. 27.
    Pohl, K., Böckle, G., van der Linden, F.J.: Software product line engineering: foundations, principles, and techniques. Springer (2005)Google Scholar
  28. 28.
    Karagiannis, D., Kühn, H.: Metamodelling Platforms. In: Bauknecht, K., Tjoa, A.M., Quirchmayr, G. (eds.) EC-Web 2002. LNCS, vol. 2455, p. 182. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  29. 29.
    Lawton, M.P., Brody, E.M.: Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 9, 179–186 (1969)CrossRefGoogle Scholar
  30. 30.
    Olivé, A., Raventós, R.: Modeling events as entities in object-oriented conceptual modeling languages. Data & Knowledge Engineering 58, 243–262 (2006)CrossRefGoogle Scholar
  31. 31.
    Moody, D.: The “Physics” of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering. IEEE Trans. Software Eng. 35, 756–779 (2009)CrossRefGoogle Scholar
  32. 32.
    Karagiannis, D.: Business Process Management: A Holistic Management Approach. In: Kop, C. (ed.) UNISON 2012. LNBIP, vol. 137, pp. 1–12. Springer, Heidelberg (2013)Google Scholar
  33. 33.
    Bolshutkin, V., Steinberger, C., Tkachuk, M.: Knowledge-Oriented Approach to Requirements Engineering in the Ambient-Assisted Living Domain. In: Kop, C. (ed.) UNISON 2012. LNBIP, vol. 137, pp. 205–207. Springer, Heidelberg (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Judith Michael
    • 1
  • Heinrich C. Mayr
    • 1
  1. 1.Application Engineering Research GroupAlpen-Adria-Universität KlagenfurtAustria

Personalised recommendations