EasyLiving: Technologies for Intelligent Environments
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
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
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Hierarchical hidden Markov models with general state hierarchy
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Multi-graph Based Semi-supervised Learning for Activity Recognition
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
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
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
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In this paper, we introduce a novel method for human activity recognition that benefits from the structure and sequential properties of the test data as well as the training data. In the training phase, we obtain a fraction of data labels at constant time intervals and use them in a semi-supervised graph-based method for recognizing the user's activities. We use label propagation on a k-nearest neighbor graph to calculate the probability of association of the unlabeled data to each class in this phase. Then we use these probabilities to train an HMM in a way that each of its hidden states corresponds to one class of activity. These probabilities are used to learn the transition probabilities between hidden states of the HMM which is used to predict the classes of the test data. Experimental results shows that the proposed method consistently outperforms the existing state of the art semi-supervised methods.