In this paper we present a software framework which supports the construction of mixed-fidelity (from sketch-based to software) prototypes for mobile devices. The framework is available for desktop computers and mobile devices (e.g., PDAs, Smartphones). It operates with low-fidelity sketch based prototypes or mid to high-fidelity prototypes with some range of functionality, providing several dimensions of customization (e.g., visual components, audio/video files, navigation, behavior) and targeting specific usability concerns. Furthermore, it allows designers and users to test the prototypes on actual devices, gathering usage information, both passively (e.g., logging) and actively (e.g., questionnaires/Experience Sampling). Overall, it conveys common prototyping procedures with effective data gathering methods that can be used on ubiquitous scenarios supporting in-situ prototyping and participatory design on-the-go. We address the framework's features and its contributions to the design and evaluation of applications for mobile devices and the field of mobile interaction design, presenting real-life case studies and results.

This paper studies the discrimination of electroencephalographic (EEG) signals based in their capacity to identify silent attentive visual reading activities versus non reading states.The use of physiological signals is growing in the design of interactive systems due to their relevance in the improvement of the coupling between user states and application behavior.Reading is pervasive in visual user interfaces. In previous work, we integrated EEG signals in prototypical applications, designed to analyze reading tasks. This work searches for signals that are most relevant for reading detection procedures. More specifically, this study determines which features, input signals, and frequency bands are more significant for discrimination between reading and non-reading classes. This optimization is critical for an efficient and real time implementation of EEG processing software components, a basic requirement for the future applications.We use probabilistic similarity metrics, independent of the classification algorithm. All analyses are performed after determining the power spectrum density of delta, theta, alpha, beta and gamma rhythms. The results about the relevance of the input signals are validated with functional neurosciences knowledge.The experiences have been performed in a conventional HCI lab, with non clinical EEG equipment and setup. This is an explicit and voluntary condition. We anticipate that future mobile and wireless EEG capture devices will allow this work to be generalized to common applications.

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In this paper we present a software framework which supports the construction and evaluation of mixedfidelity prototypes for mobile devices. The framework is available for desktop and mobile devices and allows designers and users to 1) test the prototypes on actual devices; 2) gather usage information, both passively and actively supporting contextual and ubiquitous evaluation; 3) convey common prototyping procedures with effective data gathering methods that can be used on ubiquitous scenarios; 4) support in-situ prototyping and participatory design on-the-go. We address the framework's features and its contributions to the evaluation of applications for mobile devices and the field of mobile interaction design, presenting real-life case studies and achieved results.