Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/102321
Title: | The Arousal video Game AnnotatIoN (AGAIN) dataset |
Authors: | Melhart, David Liapis, Antonios Yannakakis, Georgios N. |
Keywords: | Computer games Human-computer interaction Data sets Emotion recognition |
Issue Date: | 2022 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Melhart, D., Liapis, A. & Yannakakis, G. N. (2022). The Arousal video Game AnnotatIoN (AGAIN) dataset. IEEE Transactions on Affective Computing, |
Abstract: | How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towards general affective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1,100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modelling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest—over 37 hours of annotated video and game logs—and most diverse publicly available affective dataset based on games as interactive affect elicitors. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/102321 |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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The_Arousal_Video_Game_AnnotatIoN_AGAIN_Dataset_2022.pdf Restricted Access | 5.27 MB | Adobe PDF | View/Open Request a copy |
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