The FUNii Database: A Physiological, Behavioral, Demographic and Subjective Video Game Database for Affective Gaming and Player Experience Research

Beaudoin-Gagnon, Nicolas; Fortin-Côté, Alexis; Chamberland, Cindy; Lefebvre, Ludovic; Bergeron-Boucher, Jérémy; Campeau-Lecours, Alexandre; Tremblay, Sébastien; Jackson, Philip L.

doi:10.1109/acii.2019.8925502

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…It is heavily documented and readily accessible after the submission of a terms of use agreement. The FUNii database [4] contains physiological data from 190 players during gameplay of Ubisoft's Assassin's Creed Unity [33] and Assassin's Creed Syndicate [34]. The first dataset of its kind in games, it features datatypes such as electrocardiography, electrodermal activity, and electromyography.…”

Section: Human User Datasetsmentioning

confidence: 99%

Replicability and Transparency for the Creation of Public Human User Video Game Datasets

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Guarese

Fayek

et al. 2023

2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

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Replicability is absent in games research; a lack of transparency in protocol detail hinders scientific consensus and willingness to publish public datasets, impacting the application of these techniques in video games research. To combat this, we propose and give an example of the use of a set of experimental considerations, such as games and materials choice. This work promotes the communication of research protocols when publishing datasets, benefiting researchers when designing experiments.

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Section: Human User Datasetsmentioning

confidence: 99%

Replicability and Transparency for the Creation of Public Human User Video Game Datasets

Pretty

Guarese

Fayek

et al. 2023

2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

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“…Passive audiovisual elicitors are a popular choice as they do not require any particular skill from the participants and are relatively easy to implement. In contrast, we meet datasets that make use of active elicitors involving tasks in dyads and videogamesincluding RELOCA [44] and player experience datasets such as PED [46] or the FUNii Database [47]. Compared to passive elicitors, these interactive tasks provide a more complex and multifaceted affective stimulus, while organically structuring the participants' experience.…”

Section: Audiovisual Affective Datasetsmentioning

confidence: 99%

“…Most affective computing databases surveyed (see tenth row of Table II) capture affective dimensions such as arousal and valence, with some datasets offering labels for additional dimensions-such as dominance-and categorical labels. The surveyed datasets that have used games as affect elicitors-Mazeball [45], PED [46], and FUNii [47]-tend to be less focused with regards to the labels used and instead aim to capture more complex game-related user states such as engagement, fun or challenge. This core difference makes such player experience datasets distinctive to affective computing primarily because any lessons learned on traditional affective databases are not directly applicable to player experience datasets, and vice versa.…”

Section: Audiovisual Affective Datasetsmentioning

confidence: 99%

“…This does not advance research on general affect modelling, as researchers have to examine dissimilar datasets [2] that often comprise mismatching data collection methods and annotation tools or do not offer enough context variety (e.g. the FUNii Database [47] features two similar games from the same franchise). AGAIN addresses the aforementioned limitations by offering a large-scale corpus that is based on a set of dissimilar interactive affect elicitors that are annotated through a first-person protocol.…”

Section: Audiovisual Affective Datasetsmentioning

confidence: 99%

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The Arousal video Game AnnotatIoN (AGAIN) Dataset

Melhart,

Liapis,

Yannakakis

2021

Preprint

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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 Affect 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 modeling 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.

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“…In a game-playing context, people feel and ascertain something based on the brain's signals [1,2]. Therefore, emphasis is placed on the correlation between the physical laws of nature and the cerebral sensation and performance.…”

Section: Introductionmentioning

confidence: 99%

Bridging Ride and Play Comfort

et al. 2021

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The notion of comfort with respect to rides, such as roller coasters, is typically addressed from the perspective of a physical ride, where the convenience of transportation is redefined to minimize risk and maximize thrill. As a popular form of entertainment, roller coasters sit at the nexus of rides and games, providing a suitable environment to measure both mental and physical experiences of rider comfort. In this paper, the way risk and comfort affect such experiences is investigated, and the connection between play comfort and ride comfort is explored. A roller coaster ride simulation is adopted as the target environment for this research, which combines the feeling of being thrill and comfort simultaneously. At the same time, this paper also expands research on roller coaster rides while bridging the rides and games via the analogy of the law of physics, a concept currently known as motion in mind. This study’s contribution involves a roller coaster ride model, which provides an extended understanding of the relationship between physical performance and the mental experience relative to the concept of motion in mind while establishing critical criteria for a comfortable experience of both the ride and play.

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The FUNii Database: A Physiological, Behavioral, Demographic and Subjective Video Game Database for Affective Gaming and Player Experience Research

Cited by 8 publications

References 31 publications

Replicability and Transparency for the Creation of Public Human User Video Game Datasets

Replicability and Transparency for the Creation of Public Human User Video Game Datasets

The Arousal video Game AnnotatIoN (AGAIN) Dataset

Bridging Ride and Play Comfort

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