Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Conditional random fields for activity recognition in smart environments
Proceedings of the 1st ACM International Health Informatics Symposium
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Recognizing multi-user activities using wearable sensors in a smart home
Pervasive and Mobile Computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Hierarchical activity recognition using automatically clustered actions
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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As the number of elderly people in our society increases, the need of assistive technologies in home becomes urgent. Existing techniques allow elderly people to be better assisted through monitoring what goes on in smart homes and inferring their activities from sensor data via a recognition model. However, there are various cases that existing models have difficulties in accommodating relational data. In this paper, we present an application of probabilistic graphical model --Latent-Dynamic Conditional Random Field --to detect the goals of the individual subjects when observations have long range dependencies or multiple overlapping features. To validate the proposed method, we apply it to recognize activities in two different datasets which were collected in smart homes. The results demonstrate that Latent-Dynamic Conditional Random Fields favorably outperform other models, especially when there are extrinsic dynamic activities changes and intrinsic actions subactivities.