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Applied Artificial Intelligence
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Image and Vision Computing
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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A variational learning algorithm for the abstract hidden Markov model
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Large-scale localization from wireless signal strength
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Activity recognition through goal-based segmentation
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Extending continuous time Bayesian networks
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Detecting execution failures using learned action models
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Hypothesis pruning and ranking for large plan recognition problems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The hidden permutation model and location-based activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Cross-domain activity recognition
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Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
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Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Accurate and low-cost location estimation using kernels
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Robot introspection through learned hidden Markov models
Artificial Intelligence
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IEEE Transactions on Intelligent Transportation Systems
Understanding transportation modes based on GPS data for web applications
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LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
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SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
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Expert Systems with Applications: An International Journal
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UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance
Image and Vision Computing
Learning the behavior model of a robot
Autonomous Robots
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
Leveraging human behavior models to predict paths in indoor environments
Pervasive and Mobile Computing
Scent trails: countering terrorism through informed surveillance
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Activity Recognition for Dynamic Multi-Agent Teams
ACM Transactions on Intelligent Systems and Technology (TIST)
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Transportation mode detection using mobile phones and GIS information
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Modeling human behavior from simple sensors in the home
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INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
Motion-Based adaptation of information services for mobile users
UM'05 Proceedings of the 10th international conference on User Modeling
Exploiting multiple radii to learn significant locations
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Mobile context inference using low-cost sensors
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
OWL-based user preference and behavior routine ontology for ubiquitous system
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
A quantitative method for revealing and comparing places in the home
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
The impact of trace and adversary models on location privacy provided by K-anonymity
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
Human activity recognition with trajectory data in multi-floor indoor environment
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Some help on the way: opportunistic routing under uncertainty
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Location-based reasoning about complex multi-agent behavior
Journal of Artificial Intelligence Research
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This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (e.g. taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.