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)


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.


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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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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