Accuracy characterization for metropolitan-scale Wi-Fi localization
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Supporting location-aware services for mobile users with the whereabouts diary
Proceedings of the 1st international conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications
AAMPL: accelerometer augmented mobile phone localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Text Categorization with Semantic Commonsense Knowledge: First Results
Neural Information Processing
Efficient duration and hierarchical modeling for human activity recognition
Artificial Intelligence
SurroundSense: mobile phone localization using ambient sound and light
ACM SIGMOBILE Mobile Computing and Communications Review
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
Towards cooperative localization of wearable sensors using accelerometers and cameras
INFOCOM'10 Proceedings of the 29th conference on Information communications
User experiences with activity-based navigation on mobile devices
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Detecting activities from body-worn accelerometers via instance-based algorithms
Pervasive and Mobile Computing
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Ubiquitous Advertising: The Killer Application for the 21st Century
IEEE Pervasive Computing
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Applying Commonsense Reasoning to Place Identification
International Journal of Handheld Computing Research
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Location is a key element for ambient intelligence services. Due to GPS inaccuracies, inferring high level information (i.e., being at home, at work, in a restaurant) from geographic coordinates in still non trivial. In this paper we use information about activities being performed by the user to improve location recognition accuracy. Unlike traditional methods, relations between locations and activities are not extracted from training data but from an external commonsense knowledge base. Our approach maps location and activity labels to concepts organized within the ConceptNet network. Then, it verifies their commonsense proximity by implementing a bio-inspired greedy algorithm. Experimental results show a sharp increase in localization accuracy.