Modelling and predicting movements of museum visitors: A simulation framework for assessing the impact of sensor noise on model performance

We present a simulation framework to examine the impact of sensor noise on the performance of user models in the museum domain. Our contributions are: (1) models to simulate noisy visit trajectories as time-stamped sequences of (x, y) positional coordinates which reflect walking and hovering behaviour; (2) a discriminative inference model that distinguishes between hovering and walking on the basis of (simulated) noisy sensor observations; (3) a model that infers viewed exhibits from hovering coordinates; and (4) a model that predicts the next exhibit on the basis of inferred (rather than known) viewed exhibits. Our staged evaluation assesses the effect of these models (in combination with sensor noise) on inferential and predictive performance, thus shedding light on the reliability attributed to inferences drawn from sensor observations.