Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Because I carry my cell phone anyway: functional location-based reminder applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
The election algorithm for semantically meaningful location-awareness
Proceedings of the 6th international conference on Mobile and ubiquitous multimedia
Micro-Blog: sharing and querying content through mobile phones and social participation
Proceedings of the 6th international conference on Mobile systems, applications, and services
The Rise of People-Centric Sensing
IEEE Internet Computing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Proceedings of the 7th international conference on Mobile systems, applications, and services
How do people's concepts of place relate to physical locations?
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Exploiting multiple radii to learn significant locations
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Darwin phones: the evolution of sensing and inference on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Modeling people's place naming preferences in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Movement detection for power-efficient smartphone WLAN localization
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Discovering human places of interest from multimodal mobile phone data
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Indoor localization without infrastructure using the acoustic background spectrum
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Third party geolocation services in LBS: privacy requirements and research issues
Transactions on Data Privacy
Identifying important places in people's lives from cellular network data
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Employing user feedback for semantic location services
Proceedings of the 13th international conference on Ubiquitous computing
Deliberation for intuition: a framework for energy-efficient trip detection on cellular phones
Proceedings of the 13th international conference on Ubiquitous computing
Mobility prediction-based smartphone energy optimization for everyday location monitoring
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Immaterial materials: designing with radio
Proceedings of the Sixth International Conference on Tangible, Embedded and Embodied Interaction
Capturing transitions between users' semantically meaningful places using mobile devices
Proceedings of the 1st ACM workshop on Mobile systems for computational social science
Understanding identity exposure in pervasive computing environments
Pervasive and Mobile Computing
Simulating user intervention for interactive semantic place recognition with mobile devices
Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience
A high accuracy, low-latency, scalable microphone-array system for conversation analysis
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
Mining user similarity based on routine activities
Information Sciences: an International Journal
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Understanding customer malling behavior in an urban shopping mall using smartphones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
How long are you staying?: predicting residence time from human mobility traces
Proceedings of the 19th annual international conference on Mobile computing & networking
Lifestreams: a modular sense-making toolset for identifying important patterns from everyday life
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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Detecting visits to semantically meaningful places is important for many emerging mobile applications. We present PlaceSense, a place discovery algorithm suitable for mobile devices that exploits pervasive RF-beacons. By relying on separate mechanisms to detect entrance to and departure from a place and buffering overlapping data for subsequent visits, it is more robust than the state-of-the-art, especially in detecting short visits, places where people are mobile, or where inconsistent beacons are prevalent due to interference. We experimentally evaluate PlaceSense's effectiveness in discovering semantically meaningful places, and compare with other approaches that use coordinates or RF-beacon fingerprints. Our results demonstrate that PlaceSense correctly discovers 92% (compared to between 28% and 65% for previous work) of the visited places and accurately detects their entrance and departure times from both real-life and scripted data sets.