Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper introduces Hidden Markov Models, in order to anticipate the next movement of some persons. The optimal configuration of the model is determined by evaluating some movement sequences of real persons within an office building. The simulation results show accuracy in next location prediction reaching up to 92%.
Ubiquitous Computing, Context Prediction Methods, Hidden Markov Models, Neural Networks
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