Frustrating the user on purpose: a step toward building an affective computer

Abstract

Using a deliberately slow computer–game-interface to induce a state of hypothesised frustration in users, we collected physiological, video and behavioural data, and developed a strategy for coupling these data with real-world events. The effectiveness of our strategy was tested in a study with thirty six subjects, where the system was shown to reliably synchronise and gather data for affect analysis. A pattern-recognition strategy known as Hidden Markov Models was applied to each subject's physiological signals of skin conductivity and blood volume pressure in an effort to see if regimes of likely frustration could be automatically discriminated from regimes when frustration was much less likely. This pattern-recognition approach performed significantly better than random guessing at classifying the two regimes. Mouse-clicking behaviour was also synchronised to frustration-eliciting events and analysed, revealing four distinct patterns of clicking responses. We provide recommendations and guidelines for using physiology as a dependent measure for HCI experiments, especially when considering human emotions in the HCI equation.