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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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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Journal ArticleDOI
Ambient Assisted Living system for in-home monitoring of healthy independent elders
TL;DR: One of the main contributions of the proposed work is an exhaustive evaluation methodology that is integrated in the development process that is related with the capability of the system to adapt its behavior to that of the monitored elder.
Proceedings ArticleDOI
Discovering human places of interest from multimodal mobile phone data
TL;DR: An extensive set of experiments have been performed to show the benefits of using the proposed framework, using data from the real life of 8 users over 5 continuous months of natural phone usage.
Journal ArticleDOI
Representing and reasoning with situations for context-aware pervasive computing: a logic programming perspective
TL;DR: An approach encourages a high-level of abstraction for representing and reasoning with situations, and supports building context-aware systems incrementally by providing modularity and separation of concerns.
Journal ArticleDOI
Trip destination prediction based on past GPS log using a Hidden Markov Model
TL;DR: This approach drastically reduces the number of points supplied by the GPS device and it permits a ''support-map'' to be generated in which the main characteristics of the trips for each user are taken into account, which achieves total independence from a street-map database.
Journal ArticleDOI
Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies
TL;DR: Research attempting to utilize the data from accelerometers which are popularly integrated in smartphones demonstrates the potential that more accurate personal trip data derivation from smartphones can be achieved with much less burden on the respondents in the future.
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.