Multi-sensor data fusion within the belief functions framework application to smart home services

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6869 LNCS, Issue , 2011, Pages 123-134

  Pietropaoli, Bastien ^a   Dominici, Michele ^a   Weis, Frédéric ^b  

^a INRIA   (France)

^b UNIVERSITÉ DE RENNES 1   (France)

Author keywords

Belief functions theory; Data fusion; Smart home; Ubiquitous computing

Indexed keywords

ACTIVITY RECOGNITION; BELIEF FUNCTIONS THEORY; CONTEXT MODELING; LAYERED ARCHITECTURE; LEVEL OF ABSTRACTION; MULTISENSOR DATA FUSION; SMART HOME SERVICES; SMART HOMES;

AUTOMATION; DATA FUSION; INTELLIGENT BUILDINGS; UBIQUITOUS COMPUTING;

UNCERTAINTY ANALYSIS;

EID: 84887242789     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22875-9_11     Document Type: Conference Paper

References (28)

1. Aldrich, F.: Smart homes: Past, present and future. In: Harper, R. (ed.) Inside the Smart Home, pp. 17-39. Springer, London (2003)
2. Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Computing 9, 48-53 (2010)
3. Weiser, M.: Some computer science issues in ubiquitous computing. Commun. ACM 36, 75-84 (1993)
4. Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is key. Commun. ACM 48, 49-53 (2005)
5. Phidgets, http://www.phidgets.com/
6. Shelby, Z., Bormann, C.: 6LoWPAN: The Wireless Embedded Internet. John Wiley & Sons, Ltd., Chichester (2009)
7. Padovitz, A.: Context Management and Reasoning about Situations in Pervasive Computing. PhD thesis, Monash University, Australia (2006)
8. Goldman, R.P., Geib, C.W., Miller, C.A.: A New Model of Plan Recognition. Artificial Intelligence 64, 53-79 (1999)
9. Geib, C.W., Goldman, R.P.: A probabilistic plan recognition algorithm based on plan tree grammars. Artificial Intelligence 173(11), 1101-1132 (2009)
10. Dominici, M., Fréjus, M., Guibourdenche, J., Pietropaoli, B., Weis, F.: Towards a system architecture for recognizing domestic activity by leveraging a naturalistic human activity model. In: IRISA Internal Report 1977 (2011)
11. GuruPlug, http://www.globalscaletechnologies.com/t-guruplugdetails.aspx
12. Smets, P.: Theories of uncertainty. In: Press, I. (ed.) Handbook of Fuzzy Computation. IOS Press, Amsterdam (1998)
13. Dubois, D., Prade, H.: La problmatique scientifique du traitement de l'information. Rapport de recherche 02-08R, IRIT, Université Paul Sabatier, Toulouse (March 2002)
14. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
15. Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66(2), 191-234 (1994)
16. Yager, R.R.: On the dempster-shafer framework and new combination rules. Inf. Sci. 41, 93-137 (1987)
17. Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4, 244-264 (1988)
18. Murphy, C.K.: Combining belief functions when evidence conflicts. Decision Support Systems 29, 1-9 (2000)
19. Chen, L.-Z., Sh, W.-K.i., Deng, Y., Zhu, Z.-F.: A new fusion approach based on distance of evidences. Journal of Zhejiang University Science 6A(5), 476-482 (2005)
20. Lefevre, E., Colot, O., Vannoorenberghe, P.: Belief function combination and conflict management. Information Fusion 3(2), 149-162 (2002)
21. Denoeux, T.: Constructing belief functions from sample data using multinomial confidence regions. International Journal of Approximate Reasoning 42(3), 228-252 (2006)
22. Aregui, A., Denoeux, T.: Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities. Int. J. Approx. Reasoning 49, 575-594 (2008)
23. McKeever, S., Ye, J., Coyle, L., Dobson, S.: Using dempster-shafer theory of evidence for situation inference. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds.) EuroSSC 2009. LNCS, Vol. 5741, pp. 149-162. Springer, Heidelberg (2009), http://dx.doi.org/10.1007/978-3-642-04471-7-12
24. Liao, J., Bi, Y., Nugent, C.: Activity recognition for smart homes using dempster-shafer theory of evidence based on a revised lattice structure. In: Proceedings of the 2010 Sixth International Conference on Intelligent Environments, IE 2010, pp. 46-51. IEEE Computer Society, Washington, DC (2010)
25. Ricquebourg, V., Delafosse, M., Delahoche, L., Marhic, B., Jolly-Desodt, A.M., Menga, D.: Fault Detection by Combining Redundant Sensors: a Conflict Approach Within the TBM Framework. In: COGIS 2007, Cognitive systems with Interactive Sensors. Stanford University, Stanford (2007)
26. Smets, P.: Decision making in the tbm: the necessity of the pignistic transformation. Int. J. Approx. Reasoning 38(2), 133-147 (2005)
27. Smets, P.: The transferrable belief model for quantified belief representation. In: Smets, P. (ed.) Handbook of Defeasible Reasoning and Uncertainty Management Systems. Quantified Representation Uncertainty and Imprecision, Vol. 1, pp. 267-301. Kluwer Academic Publishers, Dordrecht (1998)
28. Martin, A., Osswald, C.: Generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification. CoRR abs/0806.2008 (2008)