DocumentCode :
2975189
Title :
Learning significant locations and predicting user movement with GPS
Author :
Ashbrook, Daniel ; Starner, Thad
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2002
fDate :
2002
Firstpage :
101
Lastpage :
108
Abstract :
Wearable computers have the potential to act as intelligent agents in everyday life and 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.
Keywords :
Global Positioning System; Markov processes; cooperative systems; groupware; wearable computers; GPS; Markov model; collaborative scenarios; intelligent agents; predictive model; significant locations learning; user movement prediction; wearable computers; Collaboration; Context modeling; Educational institutions; Global Positioning System; Hardware; Humans; Intelligent agent; Predictive models; Satellites; Wearable computers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wearable Computers, 2002. (ISWC 2002). Proceedings. Sixth International Symposium on
ISSN :
1530-0811
Print_ISBN :
0-7695-1816-8
Type :
conf
DOI :
10.1109/ISWC.2002.1167224
Filename :
1167224
Link To Document :
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