Beyond Recognition: Using Gesture Variation for Continuous Interaction

Gesture-based interaction is widespread in touch screen interfaces. The goal of this paper is to tap the richness of expressive variation in gesture to facilitate continuous interaction. We achieve this through novel techniques of adaptation and estimation of gesture characteristics. We describe two experiments. The first aims at understanding whether users can control certain gestural characteristics and if that control depends on gesture vocabulary. The second study uses a machine learning technique based on particle filtering to simultaneously recognize and measure variation in a gesture. With this technology, we create a gestural interface for a playful photo processing application. From these two studies, we show that 1) multiple characteristics can be varied independently in slower gestures (Study 1), and 2) users find gesture-only interaction less pragmatic but more stimulating than traditional menu-based systems (Study 2)

Insight into goal-directed movement strategies

The current paper proposes a novel method of analyzing goal-directed movements by dividing them into distinct movement intervals. We demonstrate how the description of the first and second most prominent movement intervals in terms of duration and length can provide insight into the applied movement strategies under different conditions. This method, although demonstrated for goal-directed movements, has the potential to be generalized to other types of movements, such as steering movements