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Improving Emotion Detection for Flow Measurement with a High Frame Rate Video based Approach

 
: Kannegieser, Ehm; Atorf, Daniel; Matrai, Robert

:

Lane, H. Chad (Ed.) ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
12th International Conference on Computer Supported Education, CSEDU 2020. Proceedings. Vol.2 : 2 - 4 May 2020, web-based event
Setúbal: SciTePress, 2020
ISBN: 978-989-758-417-6
pp.490-498
International Conference on Computer Supported Education (CSEDU) <12, 2020, Online>
English
Conference Paper
Fraunhofer IOSB ()
flow; Immersion; focus; emotion detection; micro-expressions; high frame rate video

Abstract
Achieving states of high focus (i.e., Flow, Immersion) in learning situations is linked with the motivation to learn. Developing a tool to measure such states could potentially be used to evaluate and improve learning system potential and thus learning effect. With this purpose in mind, correlations between physiological data and states of high focus were tried to be discovered in a prior study. Physiological data from over 40 participants was recorded and analyzed for correlations with states of high focus. However, no significant correlations between physiological data and elicited states of high focus have been found yet. Revisiting the results, it was concluded that especially the quality and density of emotion recognition data, elicited by a video-based approach might have potentially been insufficient. In this work in progress paper, a method with the intention of improving the quality and density of video data by way of implementing a high frame rate video approach is outlined, thus enabling the search for correlations of physiological data and states of high focus.

: http://publica.fraunhofer.de/documents/N-596966.html