Biomedical Data Acquisition and Processing to Recognize Emotions for Affective Learning

Armin Grünewald, Frédéric Li, Henrik Kampling, David Krönert, Jonas Pöhler, Julian Littau, Katrin Schnieber, Artur Piet, Björn Niehaves, Marcin Grzegorzek, Rainer Brück


Emotion recognition is a increasingly popular topic because of its potential applications in the field of affective learning. It allows the development of systems able to adapt themselves to the users' emotional state to improve the learner's experience and learning. In this paper, we introduce a new biomedical multi-sensor platform for realtime acquisition of physiological data comprising Temperature, Electroencephalography (EEG), Electroocculography (EOG), Galvanic Skin Response (GSR), Heart Rate and Blood Oxygen Saturation. We describe experimental scenarios for the induction of emotions relevant in a context of affective learning (happiness, frustration, boredom) to build a set of emotion-related data. We carry out a basic classification study by computing hand-crafted features on the time and frequency domains of signals, and training a Support-Vector-Machine (SVM) classifier to demonstrate the feasibility of our approach.

Original languageGerman
Title of host publication2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)
Publication date06.12.2018
ISBN (Print)9781538650431
Publication statusPublished - 06.12.2018
Event18th IEEE International Conference on BioInformatics and BioEngineering (BIBE 2018) - Taichung, Taiwan, Province of China
Duration: 29.10.201831.10.2018

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