User modeling for personalized universal appliance interaction

One of the driving applications of ubiquitous computing is universal appliance interaction. It is the ability to use arbitrary mobile devices-some of which we traditionally think of as computers (e.g. handhelds and wearables), and some of which we do not (e.g. cell phones)-to interact with arbitrary appliances such as TVs, printers, and lights. We believe that universal appliance interaction is best supported through the deployment of appliance user-interfaces (UIs) that are personalized to a user's habits and information needs. We are building a UI deployment system for universal appliance interaction to support various personalization features based on predicting a user's behavior. It is our belief that we can achieve these features in our system by modeling user actions using machine learning (ML) algorithms. The initial step in building such a system that relies on ML for prediction is to show that there are patterns in user appliance interaction. In this paper, our goal is to present evidence demonstrating these patterns.