A location predictor based on dependencies between multiple lifelog data

Abstract

In this paper, we propose a method for predicting future locations of a person by exploiting the person's past lifelog data. To predict the future location of a person has many applications such as the delivery of information related to the predicted locations: information with limited lifetimes (sales in a supermarket), weather reports, and traffic reports. Most existing methods for prediction only use historical location data, thus they can only handle regular movements; irregular movements are not considered. Our method predicts future locations by using personal calendar entries in addition to GPS(Global positioning system) data. Using calendar entries makes it possible to predict the locations associated with the irregular events indicated by the entries. We make Dynamic Bayesian Networks models for integrating these different kinds of lifelog data so as to yield better predictions. In experiments on real data, our methods can predict irregular movements successfully even with long lead-times, while matching the accuracy of existing schemes in predicting usual movements.