With the number of people suffering from Alzheimer’s Disease and Dementia expected to grow
over the next couple decades, the need for assistive technologies to help promote their independent
living is both imperative and imminent. Here, I focus specifically on the issue of wandering in
these patients and propose the basis for a mobile application that could predict when they are
wandering. I describe an approach to wandering prediction that involves the use of Hidden Markov
Model (HMM) variants to encapsulate movement patterns and distinguish low probability movement
sequences as wandering. Specifically, I consider a physics based HMM utilizing both speed and
directional information to model movement and an HMM that models movement trajectories with
smooth, polynomial curves. I describe the overall structure of these variants and evaluate their
performance on both artificial GPS data logs as well as those taken from a real individual