Person Movement Prediction Using Neural Networks
Ungerer, Theo (Prof. Dr.)
Kontext | Ubiquitous Computing
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 proposes neural prediction techniques to anticipate a person's next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The optimal configuration of the neural network is determined by evaluating movement sequences of real persons within an office building. The simulation results, obtained with one of the pre-trained neural predictors, show accuracy in next location prediction reaching up to 92%.