Abstract
Measuring human emotion from affective interaction is an important part of computing for mental health applications. Our work refers to non-obtrusive emotion measurement from eye tracking that gets increasingly ubiquitous, e.g., in Virtual Reality (VR) technology. We present a concept and first results of a feasibility study in which affectively weighted imagery of an image database is presented and integrated using discriminative observation, multi-object tracking and video-gaming. We measured attention preference for affective image classes and found relevant correlation with data extracted from a questionnaire on emotional states (MDBF) that would eventually substantiate basic valence classification from eye tracking data. The playful approach using the well-known concentration (pairs) game enables frequent repetition of the measurements in mental health care, therapeutic or pedagogical scenarios.
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Acknowledgments
The research leading to these results has received funding from the project PLAYTIME of the AAL Programme of the European Union, by the Austrian BMVIT/FFG (No. 857334), by the Styrian Fund for the Future within project SenseCity (No. 8009), as well as by the Austrian BMVIT/FFG by projects AMIGO (No. 865646) and OpenSense (No. 868218).
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Paletta, L., Dini, A., Pszeida, M. (2020). Emotion Measurement from Attention Analysis on Imagery in Virtual Reality. In: Fukuda, S. (eds) Advances in Affective and Pleasurable Design. AHFE 2019. Advances in Intelligent Systems and Computing, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-20441-9_2
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DOI: https://doi.org/10.1007/978-3-030-20441-9_2
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