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
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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
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
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
A new model of plan recognition
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning action models from plan examples using weighted MAX-SAT
Artificial Intelligence
Activity recognition via user-trace segmentation
ACM Transactions on Sensor Networks (TOSN)
Behaviour Recognition from Sensory Streams in Smart Environments
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Bayesian nonparametric modeling of user activities
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
| Hi-index | 0.00 |
A major issue in activity recognition in a sensor network is how to automatically segment the low-level signal sequences in order to optimize the probabilistic recognition models for goals and activities. Past efforts have relied on segmenting the signal sequences by hand, which is both time-consuming and error-prone. In our view, segments should correspond to atomic human activities that enable a goal-recognizer to operate optimally; the two are intimately related. In this paper, we present a novel method for building probabilistic activity models at the same time as we segment signal sequences into motion patterns. We model each motion pattern as a linear dynamic model and the transitions between motion patterns as a Markov process conditioned on goals. Our EM learning algorithm simultaneously learns the motion-pattern boundaries and probabilistic models for goals and activities, which in turn can be used to accurately recognize activities in an online phase. A major advantage of our algorithm is that it can reduce the human effort in segmenting and labeling signal sequences. We demonstrate the effectiveness of our algorithm using the data collected in a real wireless environment.