Book ChapterDOI
Inferring High-Level Behavior from Low-Level Sensors
Donald J. Patterson,Lin Liao,Dieter Fox,Henry Kautz +3 more
- pp 73-89
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TLDR
In this paper, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.Abstract:
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.read more
Citations
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Inferring Activities from Interactions with Objects
TL;DR: A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use.
Proceedings ArticleDOI
Route Prediction from Trip Observations
Jon E. Froehlich,John Krumm +1 more
TL;DR: This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle’s past trips using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days each.
Book ChapterDOI
Location-based activity recognition
Lin Liao,Dieter Fox,Henry Kautz +2 more
TL;DR: This work shows how to extract and label a person's activities and significant places from traces of GPS data and applies FFT-based message passing to perform efficient summation over large numbers of nodes in the networks.
Journal ArticleDOI
Discovering routines from large-scale human locations using probabilistic topic models
TL;DR: An unsupervised methodology based on two differing probabilistic topic models is developed and applied to the daily life of 97 mobile phone users over a 16-month period to achieve the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns.
Proceedings ArticleDOI
Destination prediction by sub-trajectory synthesis and privacy protection against such prediction
TL;DR: The privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users is considered, and an efficient algorithm to select a minimum number of locations a user has to hide on her trajectory in order to avoid privacy leak is proposed.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Estimation with Applications to Tracking and Navigation
TL;DR: Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
Journal ArticleDOI
Location systems for ubiquitous computing
TL;DR: This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field to help developers of location-aware applications better evaluate their options when choosing a location-sensing system.
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.