dc.contributor.author | Svoren, Henrik | |
dc.contributor.author | Thambawita, Vajira | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | Jakobsen, Petter | |
dc.contributor.author | Garcia-Ceja, Enrique | |
dc.contributor.author | Noori, Farzan Majeed | |
dc.contributor.author | Hammer, Hugo Lewi | |
dc.contributor.author | Lux, Mathias | |
dc.contributor.author | Riegler, Michael | |
dc.contributor.author | Hicks, Steven | |
dc.date.accessioned | 2021-01-29T13:52:33Z | |
dc.date.accessioned | 2021-03-05T15:48:37Z | |
dc.date.available | 2021-01-29T13:52:33Z | |
dc.date.available | 2021-03-05T15:48:37Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Svoren, Thambawita V, Halvorsen P, Jakobsen P, Garcia-Ceja E, Noori FM, Hammer HL, Lux M, Riegler M, Hicks S: Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros. In: Alay OA, Toni L. MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, 2020. Association for Computing Machinery (ACM) p. 309-314 | en |
dc.identifier.isbn | 978-1-4503-6845-2 | |
dc.identifier.uri | https://hdl.handle.net/10642/9900 | |
dc.description.abstract | Games are often defined as engines of experience, and they are heavily
relying on emotions, they arouse in players. In this paper, we
present a dataset called Toadstool as well as a reproducible methodology
to extend on the dataset. The dataset consists of video, sensor,
and demographic data collected from ten participants playing Super
Mario Bros, an iconic and famous video game. The sensor data is
collected through an Empatica E4 wristband, which provides highquality
measurements and is graded as a medical device. In addition
to the dataset and the methodology for data collection, we present
a set of baseline experiments which show that we can use video
game frames together with the facial expressions to predict the
blood volume pulse of the person playing Super Mario Bros. With
the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing
on reinforcement learning and multimodal data fusion. We
believe that the presented dataset can be interesting for a manifold
of researchers to explore exciting new interdisciplinary questions. | en |
dc.language.iso | en | en |
dc.publisher | Association for Computing Machinery (ACM) | en |
dc.relation.ispartof | MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference | |
dc.relation.ispartofseries | MMSys: Multimedia Systems;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference | |
dc.rights | This badge is applied to papers in which associated artifacts have been made permanently available for retrieval. | |
dc.subject | Multimedia datasets | en |
dc.subject | Neural networks | en |
dc.subject | Emotional machines | en |
dc.subject | Machine learning | en |
dc.title | Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros | en |
dc.type | Conference object | en |
dc.date.updated | 2021-01-29T13:52:32Z | |
dc.description.version | publishedVersion | en |
dc.identifier.doi | https://doi.org/10.1145/3339825.3394939 | |
dc.identifier.cristin | 1818160 | |
dc.source.isbn | 978-1-4503-6845-2 | |