Learning Significant Locations and Predicting User Movement with GPS

By Daniel Ashbrook, Thad Starner. Proceedings. Sixth International Symposium on Wearable Computers,, 2002

430 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Pedestrian localisation for indoor environments (2008, methodology): “The clustering algorithm used here is a variant of one originally described by Ashbrook and Starner for clustering GPS positions [88, 89].”
A Survey on Deep Learning for Human Mobility (2020, background): “[8] cluster GPS data into meaningful locations and incorporate them into a Markov model to predict individuals’ future movements.”
Mining significant semantic locations from GPS data (2010, background): “Some recent studies [2,10,24] consider the extraction of locations from GPS records, but do not consider location signiﬁcance.”
Ego-Topo: Environment Affordances From Egocentric Video (2020, background): “Work in ubiquitous computing tracks people to see where they spend time in an environment [37, 3], and “per-sonal locations” manually speciﬁed by the camera wearer ( e.g ., my ofﬁce) can be recognized using supervised learning [13].”
Inferring High-Level Behavior from Low-Level Sensors (2003, background): “One strand of such work concerns tracking and predicting a person’s movements in outdoor settings using GPS [1,2,3,4].”
Project Lachesis: Parsing and Modeling Location Histories (2004, methodology): “If a wearable computer includes a GPS, clusters of logged GPS coordinates can be used to determine destinations of interest, and transitions between clusters can provide training data for developing a probabilistic model of personal movement [ 1 ].”