Intelligent Patient Management pp 315-332

Part of the Studies in Computational Intelligence book series (SCI, volume 189)

Uncertain Information Management for ADL Monitoring in Smart Homes

  • Xin Hong
  • Chris Nugent
  • Weiru Liu
  • Jianbing Ma
  • Sally McClean
  • Bryan Scotney
  • Maurice Mulvenna

Summary

Smart Homes offer improved living conditions and levels of independence for the elderly population who require support with both physical and cognitive functions. Sensor technology development and communication networking have been well explored within the area of smart living environments to meet the demands for ageing in place. In contrast, information management still faces a challenge to be practically sound. In our current research we deploy the Dempster-Shafer theory of evidence to represent and reason with uncertain sensor data along with revision and merging techniques to resolve inconsistencies among information from different sources. We present a general framework for sensor information fusion and knowledge revision/merging especially for monitoring activities of daily living in a smart home.

Keywords

Smart sensorised living environment uncertainty information fusion belief revision belief merging DS theory epistemic state ordinal conditional function 

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References

  1. 1.
    Ageing and life course. World Health Organisation (last accessed June 2008), http://www.who.int/ageing/en/
  2. 2.
    Nugent, C.D., Finlay, D.D., Fiorini, P., Tsumaki, Y., Prassier, E.: Home automation as a means of independent living. IEEE Transactions on Automation Science and Engineering 5(1), 1–9 (2008)CrossRefGoogle Scholar
  3. 3.
    Cook, D.J., Das, S.K.: How smart are our environments? an updated look at the state of the art. Pervasive and Mobile Computing 3, 53–73 (2007)CrossRefGoogle Scholar
  4. 4.
    Mozer, M.C.: Lessons from an adaptive home. In: Smart Environments: Technology, Protocols, and Applications, pp. 273–298. Wiley, Chichester (2004)Google Scholar
  5. 5.
    Pollack, M.E.: Intelligent technology for an aging population. AI Magazine 26(2), 9–24 (2005)Google Scholar
  6. 6.
    Nugent, C.D., Mulvenna, M., Hong, X.: Experiences in the development of a smart lab. International Journal of Biomedical Engineering and Technology (in press) (2008)Google Scholar
  7. 7.
    Bucks, R.S., Ashworth, D.I., Wilcock, G.K., Siegfried, K.: Assessment of activities of daily living in dementia: development of the britstol activities of daily living scale. Age and Ageing 25, 113–120 (1996)CrossRefGoogle Scholar
  8. 8.
    Philipose, M., Fishkin, K.P., Patterson, M.P., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Computing 3(4), 50–57 (2004)CrossRefGoogle Scholar
  9. 9.
    Hong, X., Nugent, C.D., McClean, S.I., Scotney, B.W., Mulvenna, M., Devlin, S.: Evidential fusion of sensor data for activity recognition in smart homes. Pervasive and Mobile Computing (in press) (2008)Google Scholar
  10. 10.
    Dempster, A.P.: A generalization of bayesian inference. J. Roy. Statist. Soc. 30, Series B, 205–247 (1968)MathSciNetGoogle Scholar
  11. 11.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  12. 12.
    McClean, S.I., Scotney, B.W.: Using evidence theory for the integration of distributed databases. International Journal of Intelligent Systems 12, 763–776 (1997)CrossRefGoogle Scholar
  13. 13.
    Liu, W.: Analyzing the degree of conflict among belief functions. Artificial Intelligence 170(11), 909–924 (2006)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Yager, R.R., Engemann, K.J., Filev, D.P.: On the concept of immediate probabilities. International Journal of Intelligent Systems 10, 373–397 (1995)MATHCrossRefGoogle Scholar
  15. 15.
    Lowrance, J.D., Garvey, T.D., Strat, T.M.: A framework for evidential reasoning systems. In: Proceedings of the 5th AAAI, pp. 896–903 (1986)Google Scholar
  16. 16.
    Hong, X.: Heuristic Knowledge Representation and Evidence Combination Parallelization. PhD thesis, University of Ulster (2001)Google Scholar
  17. 17.
    Liu, W., Hong, J., McTear, M.F., Hughes, J.G.: An extended framework for evidential reasoining systems. International Journal of Pattern Recognition and Artificial Intelligence 7(3), 441–457 (1993)CrossRefGoogle Scholar
  18. 18.
    Alchourrón, C.E., Gärdenfors, P., Makinson, D.: On the logic of theory change: Partial meet functions for contraction and revision. Symbolic Logic 50, 510–530 (1985)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Katsuno, H., Mendelzon, A.O.: Propositional knowledge base revision and minimal change. Artificial Intelligence 52, 263–294 (1991)MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Darwiche, A., Pearl, J.: On the logic of iterated belief revision. Artificial Intelligence 89, 1–29 (1997)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Nayak, A.C., Pagnucco, M., Peppas, P.: Dynamic belief revision operators. Artificial Intelligence 146, 193–228 (2003)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Booth, R., Meyer, T.: Admissible and restrained revision. Artificial Intelligence Research 26, 127–151 (2006)MathSciNetGoogle Scholar
  23. 23.
    Jin, Y., Thielscher, M.: Iterated belief revision, revised. Artificial Intelligence 171, 1–18 (2007)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Spohn, W.: Ordinal conditional functions: A dynamic theory of epistemic states. In: Harper, W., Skyrms, B. (eds.) Causation in Decision, Belief Change, and Statistics, vol. 2, pp. 105–134. Kluwer Academic Publishers, Dordrecht (1988)Google Scholar
  25. 25.
    Dubois, D., Prade, H.: Belief revision and updates in numerical formalisms: An overview, with new results for the possibilistic framework. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 1993), pp. 620–625 (1993)Google Scholar
  26. 26.
    Friedman, N., Halpern, J.Y.: Plausibility measures: a user’s guide. In: Proceedings of UAI 1995, pp. 175–184 (1995)Google Scholar
  27. 27.
    Ma, J., Liu, W.: A general model for epistemic state revision using plausibility measures. In: Proceedings of ECAI 2008, pp. 356–360 (2008)Google Scholar
  28. 28.
    Jeffrey, R.C.: The Logic of Decision. McGraw-Hill, New York (1965); University of Chicago Press, Chicago, IL (2nd edn., 1983) (Paperback correction) (1990)Google Scholar
  29. 29.
    Halpern, J.Y.: Reasoning about Uncertainty. The MIT Press, Cambridge (2003)MATHGoogle Scholar
  30. 30.
    Konieczny, S., Pino-Pérez, R.: On the logic of merging. In: Cohn, A.G., Schubert, L., Shapiro, S.C. (eds.) KR 1998, Principles of Knowledge Representation and Reasoning, pp. 488–498. Morgan Kaufmann, San Francisco (1998)Google Scholar
  31. 31.
    Meyer, T.: Merging epistemic states. In: Mizoguchi, R., Slaney, J.K. (eds.) PRICAI 2000. LNCS, vol. 1886, pp. 286–296. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  32. 32.
    Meyer, T., Ghose, A., Chopra, S.: Social choice, merging and elections. In: Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp. 466–477 (2001)Google Scholar
  33. 33.
    Benferhat, S., Cayrol, C., Dubois, D., Lang, J., Prade, H.: Inconsistency management and prioritized syntax-based entailment. In: Proc. of IJCAI 1993, pp. 640–647 (1993)Google Scholar
  34. 34.
    Benferhat, S., Lagrue, S., Papini, O.: Revision of partially ordered information: Axiomatization, semantics and iteration. In: Proc. of IJCAI 2005, pp. 376–381 (2005)Google Scholar
  35. 35.
    Brewka, G.: A rank based description language for qualitative preferences. In: Proc. of ECAI 2004, pp. 303–307 (2004)Google Scholar
  36. 36.
    Delgrande, J., Dubois, D., Lang, J.: Iterated revision as prioritized merging. In: Proc. of KR 2006, pp. 210–220 (2006)Google Scholar
  37. 37.
    Qi, G., Liu, W., Bell, D.A.: Merging stratified knowledge bases under constraints. In: Proceedings of AAAI 2006, pp. 281–286 (2006)Google Scholar
  38. 38.
    Laverny, N., Lang, J.: From knowledge-based programs to graded belief based programs, part i: online reasoning. Synthese 147(2), 277–321 (2005)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xin Hong
    • 1
  • Chris Nugent
    • 1
  • Weiru Liu
    • 2
  • Jianbing Ma
    • 2
  • Sally McClean
    • 3
  • Bryan Scotney
    • 3
  • Maurice Mulvenna
    • 1
  1. 1.School of Computing and Mathematics and Computer Science Research InstituteUniversity of UlsterJordanstownNorthern Ireland
  2. 2.School of Computer ScienceQueen’s University BelfastNorthern Ireland
  3. 3.School of Computing and Information Engineering and Computer Science Research InstituteUniversity of UlsterColeraineNorthern Ireland

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