Intelligent User Interfaces can benefit from having knowledge on the user's emotion. However, current implementations to detect affective states, are often constraining the user's freedom of movement by instrumenting her with sensors. This prevents affective computing from being deployed in naturalistic and ubiquitous computing contexts. In this paper, we present a novel system called mASqUE, which uses a set of association rules to infer someone's affective state from their body postures. This is done without any user instrumentation and using off-the-shelf and non-expensive commodity hardware: a depth camera tracks the body posture of the users and their postures are also used as an indicator of their openness. By combining the posture information with physiological sensors measurements we were able to mine a set of association rules relating postures to affective states. We demonstrate the possibility of inferring affective states from body postures in ubiquitous computing environments and our study also provides insights how this opens up new possibilities for IUI to access the affective states of users from body postures in a nonintrusive way.

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We present two ongoing research efforts, both aim to support the use of models for designing (multi- and multiple-device) User Interfaces. The first tool, a part of the Dygimes framework, shows how context and tasks can be combined. It allows to generate prototype interfaces from context-sensitive task models. It builds upon a runtime environment in Java and an XML-based High Level User Interface Description (HLUID) language to generate the prototype interface. The second tool, uiml.net, experiments with another HLUID and another runtime enviroment to generate interfaces. Both tools are work in progress.

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