The Journal of Machine Learning Research
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Mobile user movement prediction using bayesian learning for neural networks
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Discovering human routines from cell phone data with topic models
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Large-scale localization from wireless signal strength
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Socialmotion: measuring the hidden social life of a building
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Mobility detection using everyday GSM traces
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and discovering occupancy patterns in sensor networks using latent dirichlet allocation
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
"All-about" diaries: concepts and experiences
Proceedings of the 5th International Conference on Communication System Software and Middleware
Location-based topic evolution
Proceedings of the 1st international workshop on Mobile location-based service
Extracting urban patterns from location-based social networks
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Learning to Infer the Status of Heavy-Duty Sensors for Energy-Efficient Context-Sensing
ACM Transactions on Intelligent Systems and Technology (TIST)
Transfer learning for activity recognition via sensor mapping
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Predicting future locations with hidden Markov models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Exploring trajectory-driven local geographic topics in foursquare
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Mining user similarity based on routine activities
Information Sciences: an International Journal
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
Discovery of clinical pathway patterns from event logs using probabilistic topic models
Journal of Biomedical Informatics
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In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events.