Context-Aware Middleware for Activity Recognition

Smart phones and other mobile devices have a simple notion of context largely restricted to temporal and spatial coordinates. Service providers and enterprise administrators can deploy systems incorporating activity and relations context to enhance the user experience, but this raises considerable collaboration, trust and privacy issues between different service providers. Our work is an initial step toward enabling devices themselves to represent, acquire and use a richer notion of context that includes functional and social aspects such as co-located social organizations, nearby devices and people, typical and inferred activities, and the roles people fill in them. We describe a system that learns to recognize richer contexts using sensor data from a person's Android phone along with annotations on her calendar and general background knowledge. Classifier models predict the individual users’ context with respect to a mid-level detailed activity he is performing like ‘Listening a Talk’, ‘Walking’, ‘Sleeping’, etc. We report on an evaluation of the individual and generic models in the University setting for predicting context.

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context, learning, mobile, smartphone


University of Maryland, Baltimore County

Department of Computer Science and Electrical Engineering

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