Partially hidden Markov models for privacy-preserving modeling of indoor trajectories

Markov models are natural tools for modeling trajectories, following
the principle that recent location history is predictive of
near-future directions. In this work we study Markov models
for describing and predicting human movement in indoor spaces, with
the goal of modeling the movement on a coarse scale to protect the
privacy of the individuals. Modern positioning devices, however,
provide location information on a much more finer scale.
To utilize this additional information we develop a novel
family of partially hidden Markov models that couple each observed
state with an auxiliary side information vector characterizing the
movement within the coarse grid cell. We implement the
model as a non-parametric Bayesian model and demonstrate it on
real-world trajectory data collected in a hypermarket.