Abstract
Dempster-Shafer (DS) theory, and its associated Dempster rule of combination, has been widely used to determine belief based on uncertain evidence sources. Variations to the original Dempster rule of combination have appeared in the literature to support particular scenarios where unreliable results may result from the use of original DS theory. While theoretical explanations of the rule variations are explained, there is a lack of empirical comparisons of the DS theory and its variations against real data sets. In this work, we examine several variations to DS theory. Using two real-world sensor data sets, we compare the performance of DS theory and several of its variations in recognising situations. The empirical results shed insight on how to select these fusion rules based on the nature of sensor data, the relationship of this data over time to the higher level hypotheses and the choice of frame of discernment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dobson, S., Nixon, P.: Whole-System Programming of Adaptive Ambient Intelligence. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 73–81. Springer, Heidelberg (2007)
McKeever, S., Ye, J., Coyle, L., Dobson, S.: A Context Quality Model to Support Transparent Reasoning with Uncertain Context. In: Rothermel, K., Fritsch, D., Blochinger, W., Dürr, F. (eds.) QuaCon 2009. LNCS, vol. 5786, pp. 65–75. Springer, Heidelberg (2009)
McKeever, S., Ye, J., Coyle, L., Dobson, S.: Activity recognition using temporal evidence theory. Journal of Ambient Intelligence and Smart Environments 2 (2010)
Wu, H., Siegel, M., Stiefelhagen, R.: Sensor fusion using Dempster-Shafer theory. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 7–12 (2002)
Zhang, D., Guo, M., Zhou, J., Kang, D., Cao, J.: Context reasoning using extended evidence theory in pervasive computing environments. Future Gener. Comput. Syst. 26, 207–216 (2010)
Yager, R.: On the Dempster-Shafer framework and new combination rules. Inf. Sci. 41, 93–137 (1987)
Dubois, D., Prade, H.: Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4, 244–264 (1988)
Murphy, C.: Combining belief functions when evidence conflicts. Decis. Support Syst. 29, 1–9 (2000)
Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)
Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., Malm, E.J.: Managing context information in mobile devices. IEEE Pervasive Computing 2, 42–51 (2003)
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)
Ranganathan, A., Al-Muhtadi, J., Campbell, R.: Reasoning about uncertain contexts in pervasive computing environments. IEEE Magazine on Pervasive Computing 3, 62–70 (2004)
Gu, T., Pung, H., Zhang, D.: A Bayesian approach for dealing with uncertain contexts. In: Pervasive 2004, pp. 205–210 (2004)
Clarkson, B., Pentland, A., Mase, K.: Recognizing User Context via Wearable Sensors. In: IEEE International Symposium on Wearable Computers, p. 69 (2000)
Choujaa, D., Dulay, N.: Activity Inference through Sequence Alignment. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds.) LoCA 2009. LNCS, vol. 5561, pp. 19–36. Springer, Heidelberg (2009)
Van Kasteren, T., Noulas, A., Englebienne, G., Krose, B.: Accurate activity recognition in a home setting. In: Ubicomp 2008, pp. 1–9 (2008)
Wu, H.: Sensor fusion using dempster shafer theory. PhD Thesis (2003)
Wu, H., Siegel, M., Stiefelhagen, R.: Sensor fusion using Dempster-Shafer theory. IEEE Instrumentation and Measurement 1, 7–12 (2002)
Hong, X., Nugent, C., Liu, W., Ma, J., McClean, S., Scotney, B., Mulvenna, M.: Uncertain Information Management for ADL Monitoring in Smart Homes. In: McClean, S., Millard, P., El-Darzi, E., Nugent, C. (eds.) Intelligent Patient Management. SCI, vol. 189, pp. 315–332. Springer, Heidelberg (2009)
Hong, X., Nugent, C., Mulvenna, M., Scotney, B., McClean, S., Devlin, B.: Evidential fusion of sensor data for activity recognition in smart homes. Pervasive and Mobile Computing 5, 236–252 (2009)
Zadeh, L.: A Simple View of the Dempster-Shafer Theory of Evidence and its Implication for the Rule of Combination. AI Magazine 7, 85–90 (1986)
Sentz, K., Ferson, S.: Combination of Evidence in Dempster Shafer Theory. Sandia National Laboratories tech-report (2002)
McKeever, S.: PhD Thesis: Recognising Situations using extended Dempster-Shafer Theory. phdthesis (2011)
Liu, W.: Analyzing the degree of conflict among belief functions. Artif. Intell. 170, 909–924 (2006)
Smets, P., Kennes, R.: The Transfer Belief Model, Symbolic and Quantitative Approaches to Uncertainty, pp. 91–96 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing
About this paper
Cite this paper
McKeever, S., Ye, J. (2013). A Comparison of Evidence Fusion Rules for Situation Recognition in Sensor-Based Environments. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-04406-4_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04405-7
Online ISBN: 978-3-319-04406-4
eBook Packages: Computer ScienceComputer Science (R0)
