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EVA1 is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities. EVA requires a multidisciplinary approach, combining a number of critical building blocks into a cybernetics systems/software architecture: emotion aware systems and algorithms, multimodal interaction design, cognitive modelling, decision making and recommender systems, emotion sensing as feedback for learning, and distributed (edge) computing delivering cognitive services.
Driver state detection is an emerging topic for automotive user interfaces. Motivated by the trend of self-tracking, one crucial question within this field is how or whether detected states should be displayed. In this work we investigate the impact of demographics and personality traits on the user experience of driver state visualizations. 328 participants experienced three concepts visualizing their current state in a publicly installed driving simulator. Driver age, experience, and personality traits were shown to have impact on visualization preferences. While a continuous display was generally preferred, older respondents and drivers with little experience favored a system with less visual elements. Extroverted participants were more open towards interventions. Our findings lead us to believe that, while users are generally open to driver state detection, its visualization should be adapted to age, driving experience, and personality. This work is meant to support professionals and researchers designing affective in-car information systems.
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