Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Discovering personal gazetteers: an interactive clustering approach
Proceedings of the 12th annual ACM international workshop on Geographic information systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Determining transportation mode on mobile phones
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
Ambulation: A Tool for Monitoring Mobility Patterns over Time Using Mobile Phones
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
SystemSens: a tool for monitoring usage in smartphone research deployments
MobiArch '11 Proceedings of the sixth international workshop on MobiArch
Journal of Biomedical Informatics
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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Smartphones can capture diverse spatio-temporal data about an individual; including both intermittent self-report, and continuous passive data collection from onboard sensors and applications. The resulting personal data streams can support powerful inference about the user's state, behavior, well-being and environment. However making sense and acting on these multi-dimensional, heterogeneous data streams requires iterative and intensive exploration of the datasets, and development of customized analysis techniques that are appropriate for a particular health domain. Lifestreams is a modular and extensible open-source data analysis stack designed to facilitate the exploration and evaluation of personal data stream sense-making. Lifestreams analysis modules include: feature extraction from raw data; feature selection; pattern and trend inference; and interactive visualization. The system was iteratively designed during a 6-month pilot in which 44 young mothers used an open-source participatory mHealth platform to record both self-report and passive data about their diet, stress and exercise. Feedback as participants and the study coordinator attempted to use the Lifestreams dashboard to make sense of their data collected during this intensive study were critical inputs into the design process. In order to explore the generality and extensibility of Lifestreams pipeline, it was then applied to two additional studies with different datasets, including a continuous stream of audio data, self-report data, and mobile system analytics. In all three studies, Lifestreams' integrated analysis pipeline was able to identify key behaviors and trends in the data that were not otherwise identified by participants.