Recognizing emotions on the basis of keystroke dynamics

The article describes a research on recognizing emotional states on the basis of keystroke dynamics. An overview of various studies and applications of emotion recognition based on data coming from keyboard is presented. Then, the idea of an experiment is presented, i.e. the way of collecting and labeling training data, extracting features and finally training classifiers. Different classification approaches are proposed to be tested: universal vs. individual models, multiclass vs. two-class. The obtained results reveal which of these approaches are appropriate for the given task. The individual two-class models turn out to be the most accurate.