Context-Aware Middleware for Activity Recognition

Smartphones 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. Geo-social locations include the concepts of 'home' and 'school' and can be extended to others like 'work' or 'a restaurant'.

Our framework combines data from the phone's sensors (GPS, WI-FI, Bluetooth, acceleration, proximity, etc.) with data mined from applications (e.g., calendar) to produce features that can be used in a machine learning system. Training data from several university students and staff was collected using a system that periodically prompted the user for her true geo-social location and activity. The resulting classifier models were used to predict the individual user's context from new sensor data. The data from a set of users was combined to create a generic model.

We report on an evaluation of the individual and generic models in the university setting for predicting context. Finally, we discuss how our extended context notion can be applied to many interesting applications for smart phone users.

Committee:

• Dr. Tim Finin (chair)
• Dr. Anupam Joshi
• Dr. Yelena Yesha
• Dr. Laura Zavala