title: Building personal maps from GPS data.
abstract: In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.
authors.affiliation: Department of Computer Science & Engineering, University of Washington, Seattle, Washington 98195, USA. liaolin@cs.washington.edu
authors.forename: Lin
authors.initials: L
authors.lastname: Liao
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Patterson
authors.forename: Dieter
authors.initials: D
authors.lastname: Fox
authors.forename: Henry
authors.initials: H
authors.lastname: Kautz
mesh_terms: D000203:Activities of Daily Living; D001519:Behavior; D003198:Computer Simulation; D006801:Humans; D007256:Information Systems; D033183:Interdisciplinary Communication; D008377:Maps as Topic