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This paper presents the concept of a fiction-driven workshop that was conducted using a process called D/A/R/E. The aim of this design-orientated approach is to address environmental issues with citizens and empower them in understanding digital technology concepts. Within the framework of an urban artist research and mediation project, a pilot study on the question 'How will we breathe tomorrow?' was devised. For this endeavour, participants were introduced to the subject of air quality and took part in the process of creating a personal data-based future artefact with sensor technology. In the spirit of Antony Dunne and Fiona Raby, these functional artefacts were used as tools to better comprehend the present and discuss a desirable future. Additionally, they were utilised to engage the broader public in environmental, digital, and ethical issues.

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Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to interand within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users' reactions can be tied to events.