Human-Centered Artificial Intelligence > Team

Automatic affect recognition: Previous solutions for affect recognition are based on non-representative and unrealistic data sets. The first step towards better emotion recognition is therefore the collection of a balanced video data set in a situation as natural as possible using standardized test procedures. With this data material we want to develop algorithms of machine learning for affect recognition. Since the data material is video data, innovative approaches to the integration of different modalities (voice, facial expression, gaze behavior) can be used .

Computer-based stress measurement: The measurement of stress has so far focused mainly on self-report or individual parameters of the physiological response. In different stress paradigms we want to record the non-verbal behavior of test persons together with physiological markers. Based on these multimodal data, we develop algorithms for automatic stress detection and validate them in natural environments.

Analysis of social embedding: To assess the social integration of a person, clinicians and researchers often use questionnaires. In order to capture the social integration of a person more sensitively and objectively, we want to develop an automatic analysis of online social interaction data. In a large online study, we compare the sensitivity of this approach to classical questionnaires. Furthermore, we identify characteristic and helpful interaction patterns using machine learning to predict the social embedding and resilience of a respondent.