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Personalized soft-keyboards which adapt to a user's individual typing behavior can reduce typing errors on interactive displays. In multi-user scenarios a personalized model has to be loaded for each participant. In this paper we describe a user identification technique that is based on raw sensor data from an optical touch screen. For classification of users we use a multi-class support vector machine that is trained with grayscale images from the optical sensor. Our implementation can identify a specific user from a set of 12 users with an average accuracy of 97.51% after one keystroke. It can be used to automatically select individual typing models during free-text entry. The resulting authentication process is completely implicit. We furthermore describe how the approach can be extended to automatic loading of personal information and settings.

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We present a multi-touch tabletop application that utilizes LEGO bricks as physical representations for musical notes to create a novel digital learning environment for musical composition principles. Our application makes use of the physical qualities that graspable objects provide and combines them with dynamic visualizations on a multi-touch screen. While the system can be used to collaboratively create melodies and harmonies, our concept specifically aims at introducing and teaching composition principles. In this paper, we illustrate the concept and the iterative process that lead to the final application design. Furthermore, we discuss findings from four composing workshops which were conducted with school children with varying levels of musical expertise.