The Journal of Machine Learning Research
Estimating Location Using Wi-Fi
IEEE Intelligent Systems
An HDP-HMM for systems with state persistence
Proceedings of the 25th international conference on Machine learning
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Activity recognition through goal-based segmentation
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Abnormal activity recognition based on HDP-HMM models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Feature engineering for semantic place prediction
Pervasive and Mobile Computing
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Human activity modeling is becoming more and more important in ubiquitous computing as it builds a foundation for higher-level applications in areas such as e-health and activity recommendation systems. Many existing works in this area focus on recognizing a pre-defined set of activities using some devices in the supervised learning setting, however, it is hard to define activities and label sensor data, especially for a new environment. In this note we aim to recognize activities in an unsupervised way - segment activity sensor reading sequence and group the segments into meaningful categories by leveraging Sticky HDP-HMM. We have conducted experiments on a sensor dataset collected in an office area using a smartphone and the result shows that our method frees annotation process and renders good activity recognition result.