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A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems

  • Rim Helaoui
  • Mathias Niepert
  • Heiner Stuckenschmidt
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 72)

Abstract

Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and continuous healthcare systems with reduced costs. A primary challenge for such assisted living systems is the automated recognition of everyday activities carried out by humans in their own home. In this work, we investigate the use of Markov Logic Networks as a framework for activity recognition within intelligent home-like environments equippedwith pervasive light-weight sensor technologies. In particular, we explore the ability of MLNs to capture temporal relations and background knowledge for improving the recognition performance.

Keywords

Hide Markov Model Recognition Accuracy Activity Recognition Hard Constraint Temporal Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Patterson, D.J., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: ISWC 2005 (2005)Google Scholar
  2. 2.
    Augusto, J.C., Liu, J., McCullagh, P., Wang, H., Yang, J.: Management of uncertainty and spatio-temporal aspects for monitoring and diagnosis in a Smart Home. In: IJCIS (2008)Google Scholar
  3. 3.
    Stikic, M., Schiele, B.: Activity recognition from sparsely labeled data using multi-instance learning. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds.) LoCA 2009. LNCS, vol. 5561, pp. 156–173. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised activity recognition using automatically mined common sense. In: AAAI (2005)Google Scholar
  5. 5.
    Choudhury, T., Philipose, M., Wyatt, D., Lester, J.: Towards activity databases: Using sensors and statistical models to summarize people’s lives. Data Eng. Bull. 29, 49–58 (2006)Google Scholar
  6. 6.
    Biswas, R., Thrun, S., Fujimura, K.: Recognizing activities with multiple cues. In: HUMO 2007, pp. 255–270 (2007)Google Scholar
  7. 7.
    Tran, S.D., Davis, L.S.: Event modeling and recognition using markov logic networks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 610–623. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Cirillo, M., Lazellotto, F., Pecora, F., Saffiotti, A.: Monitoring domestic activities with temporal constraints and components. In: Proc. IE 2009, pp. 117–124 (2009)Google Scholar
  9. 9.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity Recognition in the Home Using Simple and Ubiquitous Sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. 26, 832–843 (1983)zbMATHGoogle Scholar
  11. 11.
    Bodon, F.: A Survey on Frequent Itemset Mining, Technical Report, Budapest University of Technology and Economic (2006)Google Scholar
  12. 12.
    Achterberg, T.: Constraint Integer Programming, Ph.D. dissertation, Technische Universität Berlin (2007)Google Scholar
  13. 13.
    Hochul, J., Taehwan, K., Joongmin, C.: Ontology-Based User Intention Recognition for Proactive Planning of Intelligent Robot Behavior. In: MUE, pp. 158–175 (2008)Google Scholar
  14. 14.
    Helaoui, R.: Towards a Proactive System Based on Activity Recognition. In: PerCom (to appear 2010)Google Scholar
  15. 15.
    Riedel, S.: Improving the accuracy and efficiency of map inference for markov logic. In: Proc. of UAI, pp. 468–475 (2008)Google Scholar
  16. 16.
    Biba, M., Ferilli, S., Esposito, F.: Structure Learning of Markov Logic Networks through Iterated Local Search. In: ECAI, pp. 361–365 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rim Helaoui
    • 1
  • Mathias Niepert
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.KR & KM Research Group, Computer Science InstituteUniversity of MannheimGermany

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