Transsituational Individual-Specific Biopsychological Classification of Emotions

Abstract

The goal of automatic biopsychological emotion recognition of companion technologies is to ensure reliable and valid classification rates. In this paper, emotional states were induced via a Wizard-of-Oz mental trainer scenario, which is based on the valence-arousal-dominance model. In most experiments, classification algorithms are tested via leave-out cross-validation of one situation. These studies often show very high classification rates, which are comparable with those in our experiment (92.6%). However, in order to guarantee robust emotion recognition based on biopsychological data, measurements have to be taken across several situations with the goal of selecting stable features for individual emotional states. For this purpose, our mental trainer experiment was conducted twice for each subject with a 10-min break between the two rounds. It is shown that there are robust psychobiological features that can be used for classification (70.1%) in both rounds. However, these are not the same as those that were found via feature selection performed only on the first round (classification: 53.0%).

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