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Prediction of Indoor Movements Using Bayesian Networks

  • Jan Petzold
  • Andreas Pietzowski
  • Faruk Bagci
  • Wolfgang Trumler
  • Theo Ungerer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3479)

Abstract

This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as well as memory and performance requirements of a Bayesian network predictor. The results are compared with further context predictor approaches – a state predictor and a multi-layer perceptron predictor using exactly the same evaluation set-up and benchmarks. The publicly available Augsburg Indoor Location Tracking Benchmarks are applied as predictor loads. Our results show that the Bayesian network predictor reaches a next location prediction accuracy of up to 90% and a duration prediction accuracy of up to 87% with variations depending on the person and specific predictor set-up. The Bayesian network predictor performs in the same accuracy range as the neural network and the state predictor.

Keywords

Prediction Accuracy Bayesian Network Location Prediction Dynamic Bayesian Network Prediction Technique 
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.
    Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)CrossRefGoogle Scholar
  2. 2.
    Behrends, E.: Introduction to Marcov Chains. Vieweg (1999)Google Scholar
  3. 3.
    Bhattacharya, A., Das, S.K.: LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks 8, 121–135 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Gopalratnam, K., Cook, D.J.: Active LeZi: An Incremental Parsing Algorithm for Sequential Prediction. In: Sixteenth International Florida Artificial Intelligence Research Society Conference, pp. 38–42. St. Augustine, Florida (2003)Google Scholar
  5. 5.
    Gurney, K.: An Introduction to Neural Networks. Routledge, New York (2002)Google Scholar
  6. 6.
    Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996)Google Scholar
  7. 7.
    Kaowthumrong, K., Lebsack, J., Han, R.: Automated Selection of the Active Device in Interactive Multi-Device Smart Spaces. In: Workshop at UbiComp 2002: Supporting Spontaneous Interaction in Ubiquitous Computing Settings, Göteborg, Sweden (2002)Google Scholar
  8. 8.
    Katsiri, E.: Principles of Context Inference. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 33–34. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Mayrhofer, R.: An Architecture for Context Prediction. In: Advances in Pervasive Computing, number 3-85403-176-9. Austrian Computer Society (OCG) (April 2004)Google Scholar
  10. 10.
    Mozer, M.C.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: AAAI Spring Symposium on Intelligent Environments, Menlo Park, CA, USA, pp. 110–114 (1998)Google Scholar
  11. 11.
    Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Petzold, J.: Augsburg Indoor Location Tracking Benchmarks. Technical Report 2004-9, Institute of Computer Science, University of Augsburg, Germany (February 2004), http://www.informatik.uni-augsburg.de/skripts/techreports/
  13. 13.
    Petzold, J.: Augsburg Indoor Location Tracking Benchmarks. Context Database. Institute of Pervasive Computing. University of Linz, Austria (January 2005), http://www.soft.uni-linz.ac.at/Research/Context_Database/index.php
  14. 14.
    Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Confidence estimation of the state predictor method. In: Markopoulos, P., Eggen, B., Aarts, E., Crowley, J.L. (eds.) EUSAI 2004. LNCS, vol. 3295, pp. 375–386. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Petzold, J., Bagci, F., Trumler, W., Ungerer, T., Vintan, L.: Global State Context Prediction Techniques Applied to a Smart Office Building. In: The Communication Networks and Distributed Systems Modeling and Simulation Conference, San Diego, CA, USA (January 2004)Google Scholar
  16. 16.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE 77(2) (February 1989)Google Scholar
  17. 17.
    Trumler, W., Bagci, F., Petzold, J., Ungerer, T.: Smart Doorplate. In: First International Conference on Appliance Design (1AD), Bristol, GB (May 2003); Reprinted in Pers. Ubiquit. Comput. 7, 221–226 (2003)Google Scholar
  18. 18.
    Vintan, L., Gellert, A., Petzold, J., Ungerer, T.: Person Movement Prediction Using Neural Networks. In: First Workshop on Modeling and Retrieval of Context, Ulm, Germany (September 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jan Petzold
    • 1
  • Andreas Pietzowski
    • 1
  • Faruk Bagci
    • 1
  • Wolfgang Trumler
    • 1
  • Theo Ungerer
    • 1
  1. 1.Institute of Computer ScienceUniversity of AugsburgAugsburgGermany

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