The active badge location system
ACM Transactions on Information Systems (TOIS)
A Bayesian model of plan recognition
Artificial Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Detect User Activity and Availability from a Variety of Sensor Data
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Human Action Segmentation via Controlled Use of Missing Data in HMMs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Adaptive Temporal Radio Maps for Indoor Location Estimation
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
The Knowledge Engineering Review
A sensory grammar for inferring behaviors in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Hierarchical hidden Markov models with general state hierarchy
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
High-level goal recognition in a wireless LAN
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Activity recognition through goal-based segmentation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Data-driven MCMC for learning and inference in switching linear dynamic systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Where is . •.? learning and utilizing motion patterns of persons with mobile robots
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A sound and fast goal recognizer
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A new model of plan recognition
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Toward scalable activity recognition for sensor networks
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Location sensing and privacy in a context-aware computing environment
IEEE Wireless Communications
Wireless Geolocation Systems and Services
IEEE Communications Magazine
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-agent smart environments
Journal of Ambient Intelligence and Smart Environments
Coping with multiple residents in a smart environment
Journal of Ambient Intelligence and Smart Environments
Data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Multi-agent smart environments
Journal of Ambient Intelligence and Smart Environments
Coping with multiple residents in a smart environment
Journal of Ambient Intelligence and Smart Environments
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
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A major issue of activity recognition in sensor networks is automatically recognizing a user's high-level goals accurately from low-level sensor data. Traditionally, solutions to this problem involve the use of a location-based sensor model that predicts the physical locations of a user from the sensor data. This sensor model is often trained offline, incurring a large amount of calibration effort. In this article, we address the problem using a goal-based segmentation approach, in which we automatically segment the low-level user traces that are obtained cheaply by collecting the signal sequences as a user moves in wireless environments. From the traces we discover primitive signal segments that can be used for building a probabilistic activity model to recognize goals directly. A major advantage of our algorithm is that it can reduce a significant amount of human effort in calibrating the sensor data while still achieving comparable recognition accuracy. We present our theoretical framework for activity recognition, and demonstrate the effectiveness of our new approach using the data collected in an indoor wireless environment.