Affective Computing

This course introduces affective computing and its applications in various contexts. Affective computing (artificial emotional intelligence) is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. Students will get acquainted with affective computing applications, bio-signals that are used to recognize human emotions and their measuring devices as well as different emotion recognition methods.

The aim of this course is to get knowledge about Affective Computing and to get acquainted with the concept, an application and methods of Affective Computing.

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1. Definitions and affective computing applications 2. Signal sampling 3. Orthogonal signal transformations. Signal frequency analysis. Fourrier transform. Wawelet transform 4. Signal filtering 5. J.A.Russel arousal-valence model and other models 6. Biosignals and their observations. Eye tracking, skin resistance, SpO2 signals, Muscle signals 7. Human brain signals. 8. Face recognition. 9. Ethical aspects of AC.

1. Depth of knowledge in field of Affective Computing. 2. Ability to compare different methods and solutions in analysis of Affective Computing data. 3. Practical skills demonstration in signal processing and analysis. 4. Project result presentation to colleagues and the lecturer. 5. Ability to motivate selected methods used for analysis. 6. Ability to use analysis and research methods, programming tools in Affective Computing.