Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
A model for reasoning about persistence and causation
Computational Intelligence
Time series prediction using belief network models
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning and Inference in Parametric Switching Linear Dynamical Systems
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEICE - Transactions on Information and Systems
Hierarchical hidden Markov models with general state hierarchy
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple frame motion inference using belief propagation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
On-line ADL Recognition with Prior Knowledge
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Transferring knowledge of activity recognition across sensor networks
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Augmenting mobile localization with activities and common sense knowledge
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Online activity recognition using evolving classifiers
Expert Systems with Applications: An International Journal
Large-scale statistical modeling of motion patterns: a Bayesian nonparametric approach
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Intelligent Decision Technologies
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A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.