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The great variety of new (Post-WIMP) interaction styles make them difficult to evaluate and compare. We propose a new evaluation method for them, Knowledge-Based Usability Evaluation (KBUE), that is based on similar ideas to those that drive cognitive architectures, such as ACT-R and Soar. We present KBUE as a way to formally specify the knowledge in the environment and in the user's head, and how this specification can be used to examine whether the aforementioned set of knowledge covers the required knowledge for the performance of a task in a user interface. We believe that by using this specification, it becomes easier to evaluate and compare Reality-Based interfaces.

Abstract. Passive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user's mental state. The goal of the present study is to distinguish between different levels of game difficulty using non-invasive brain activity measurement with functional near-infrared spectroscopy (fNIRS). The study is designed to lead to adaptive interfaces that respond to the user's brain activity in real time. Nine subjects played two levels of the game Pacman while their brain activity was measured using fNIRS. Statistical analysis and machine learning classification results show that we can discriminate well between subjects playing or resting, and distinguish between the two levels of difficulty with some success. In contrast to most previous fNIRS studies which only distinguish brain activity from rest, we attempt to tell apart two levels of brain activity, and our results show potential for using fNIRS in an adaptive game or user interface.

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Flexible displays potentially allow for interaction styles that resemble those used in paper documents. Bending the display, e.g., to page forward, shows particular promise as an interaction technique. In this paper, we present an evaluation of the effectiveness of various bend gestures in executing a set of tasks with a flexible display. We discuss a study in which users designed bend gestures for common computing actions deployed on a smartphone-inspired flexible E Ink prototype called PaperPhone. We collected a total of 87 bend gesture pairs from ten participants and their appropriateness over twenty actions in five applications. We identified six most frequently used bend gesture pairs out of 24 unique pairs. Results show users preferred bend gestures and bend gesture pairs that were conceptually simpler, e.g., along one axis, and less physically demanding. There was a strong agreement among participants to use the same three pairs in applications: (1) side of display, up/down (2) top corner, up/down (3) bottom corner, up/down. For actions with a strong directional cue, we found strong consensus on the polarity of the bend gestures (e.g., navigating left is performed with an upwards bend gesture, navigating right, downwards). This implies that bend gestures that take directional cues into account are likely more natural to users.

We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO 2 ) and deoxyhemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44%-72%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.

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