A HMM-Based Location Prediction Framework with Location Recognizer Combining k-Nearest Neighbor and Multiple Decision Trees

  • Yong-Joong Kim
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


Knowing user’s current or next location is very important task for context-aware services in mobile environment. Many researchers have tried to predict user location using their own methods. However, they focused mainly the performance of method, and only few were considered development of real working system on mobile devices. In this paper, we present a location prediction framework, and develop a personalized destination prediction system based on this framework using smartphone. The framework consists of two methods of recognizing user location based on the combined method of k-nearest neighbor (kNN) and decision tree, and predicting user destination based on the hidden Markov model (HMM). The destination prediction system is composed of four parts including mobile sensor log collector, location recognition module, location prediction module, and system management module. Experiments on real datasets of five persons showed that our method achieved average prediction accuracy above 87%.


Location recognition Location prediction Location extraction Location- based services 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yong-Joong Kim
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

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