Non-invasive Player Experience Estimation from Body Motion and Game Context

Paolo Burelli; George Triantafyllidis; Ioannis Patras

doi:10.1109/CIG.2014.6932871

Non-invasive Player Experience Estimation from Body Motion and Game Context

Paolo Burelli, George Triantafyllidis, Ioannis Patras

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

8 Citations (Scopus)

Abstract

In this paper, we investigate on the relationship between player experience and body movements in a non-physical 3D computer game. During an experiment, the participants played a series of short game sessions and rated their experience while their body movements were tracked using a depth camera. The data collected was analysed and a neural network was trained to find the mapping between player body movements, player in- game behaviour and player experience. The results reveal that some aspects of player experience, such as anxiety or challenge, can be detected with high accuracy (up to 81%). Moreover, taking into account the playing context, the accuracy can be raised up to 86%. Following such a multi-modal approach, it is possible to estimate the player experience in a non-invasive fashion during the game and, based on this information, the game content could be adapted accordingly.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games
Number of pages	7
Publisher	IEEE Computer Society Press
Publication date	2014
Pages	1-7
ISBN (Print)	978-1-4799-3547-5, 978-1-4799-3546-8
DOIs	https://doi.org/10.1109/CIG.2014.6932871
Publication status	Published - 2014
Event	IEEE Conference on Computational Intelligence and Games - Dortmund, Germany Duration: 26 Aug 2014 → 29 Aug 2014

Conference

Conference	IEEE Conference on Computational Intelligence and Games
Country/Territory	Germany
City	Dortmund
Period	26/08/2014 → 29/08/2014

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10.1109/CIG.2014.6932871

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title = "Non-invasive Player Experience Estimation from Body Motion and Game Context",

abstract = "In this paper, we investigate on the relationship between player experience and body movements in a non-physical 3D computer game. During an experiment, the participants played a series of short game sessions and rated their experience while their body movements were tracked using a depth camera. The data collected was analysed and a neural network was trained to find the mapping between player body movements, player in- game behaviour and player experience. The results reveal that some aspects of player experience, such as anxiety or challenge, can be detected with high accuracy (up to 81%). Moreover, taking into account the playing context, the accuracy can be raised up to 86%. Following such a multi-modal approach, it is possible to estimate the player experience in a non-invasive fashion during the game and, based on this information, the game content could be adapted accordingly.",

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doi = "10.1109/CIG.2014.6932871",

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pages = "1--7",

booktitle = "Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games",

publisher = "IEEE Computer Society Press",

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note = "IEEE Conference on Computational Intelligence and Games, CIG ; Conference date: 26-08-2014 Through 29-08-2014",

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Burelli, P, Triantafyllidis, G & Patras, I 2014, Non-invasive Player Experience Estimation from Body Motion and Game Context. in Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games. IEEE Computer Society Press, pp. 1-7, IEEE Conference on Computational Intelligence and Games, Dortmund, Germany, 26/08/2014. https://doi.org/10.1109/CIG.2014.6932871

Non-invasive Player Experience Estimation from Body Motion and Game Context. / Burelli, Paolo; Triantafyllidis, George; Patras, Ioannis.
Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games. IEEE Computer Society Press, 2014. p. 1-7.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Non-invasive Player Experience Estimation from Body Motion and Game Context

AU - Burelli, Paolo

AU - Triantafyllidis, George

AU - Patras, Ioannis

PY - 2014

Y1 - 2014

N2 - In this paper, we investigate on the relationship between player experience and body movements in a non-physical 3D computer game. During an experiment, the participants played a series of short game sessions and rated their experience while their body movements were tracked using a depth camera. The data collected was analysed and a neural network was trained to find the mapping between player body movements, player in- game behaviour and player experience. The results reveal that some aspects of player experience, such as anxiety or challenge, can be detected with high accuracy (up to 81%). Moreover, taking into account the playing context, the accuracy can be raised up to 86%. Following such a multi-modal approach, it is possible to estimate the player experience in a non-invasive fashion during the game and, based on this information, the game content could be adapted accordingly.

AB - In this paper, we investigate on the relationship between player experience and body movements in a non-physical 3D computer game. During an experiment, the participants played a series of short game sessions and rated their experience while their body movements were tracked using a depth camera. The data collected was analysed and a neural network was trained to find the mapping between player body movements, player in- game behaviour and player experience. The results reveal that some aspects of player experience, such as anxiety or challenge, can be detected with high accuracy (up to 81%). Moreover, taking into account the playing context, the accuracy can be raised up to 86%. Following such a multi-modal approach, it is possible to estimate the player experience in a non-invasive fashion during the game and, based on this information, the game content could be adapted accordingly.

U2 - 10.1109/CIG.2014.6932871

DO - 10.1109/CIG.2014.6932871

M3 - Article in proceeding

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SN - 978-1-4799-3546-8

SP - 1

EP - 7

BT - Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games

PB - IEEE Computer Society Press

T2 - IEEE Conference on Computational Intelligence and Games

Y2 - 26 August 2014 through 29 August 2014

ER -

Non-invasive Player Experience Estimation from Body Motion and Game Context

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