Using GPS to learn significant locations and predict movement across multiple users

被引：676

作者：

Ashbrook, Daniel ^{[1
]}

Starner, Thad ^{[1
]}

机构：

[1] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA

来源：

PERSONAL AND UBIQUITOUS COMPUTING | 2003年 / 7卷 / 05期

基金：

美国国家科学基金会;

关键词：

Context; GPS; Location aware computing; Schedule prediction;

D O I：

10.1007/s00779-003-0240-0

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wearable computers have the potential to act as intelligent agents in everyday life and to assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.

引用

页码：275 / 286

页数：12

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