Towards Automated Player Experience Detection With Computer Vision Techniques

Tan, C; Pisan, Y

Towards Automated Player Experience Detection With Computer Vision Techniques

Tan, C Pisan, Y

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: CHI Workshop on Game User Research, 2012, pp. 2679 - 2681
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Tan, C	en_US
dc.contributor.author	Pisan, Y https://orcid.org/0000-0002-9043-5803	en_US
dc.contributor.editor	El-Nasr, MS	en_US
dc.contributor.editor	Desurvire, H	en_US
dc.contributor.editor	Nacke, LE	en_US
dc.contributor.editor	Drachen, A	en_US
dc.contributor.editor	Bernhaupt, R	en_US
dc.contributor.editor	Isbister, K	en_US
dc.contributor.editor	Calvi, L	en_US
dc.date	2012-05-05	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	CHI Workshop on Game User Research, 2012, pp. 2679 - 2681	en_US
dc.identifier.isbn	978-1-4503-1016-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22972
dc.description.abstract	There has been an increasing number of quantitative methods to measure and evaluate player experiences. However, current methods either focus on telemetry approaches, which are insucient to capture real life responses, or psychophysiological methods, which are intrusive and more suited to controlled laboratory environments. This paper presents the position that computer vision techniques can provide a less intrusive and more versatile solution for automatic evaluation of game user experiences. A conceptual framework to automatically infer flow intensity is presented and a work-in-progress study is included to demonstrate the feasibility of this research direction.	en_US
dc.publisher	ACM	en_US
dc.relation.ispartof	CHI Workshop on Game User Research	en_US
dc.title	Towards Automated Player Experience Detection With Computer Vision Techniques	en_US
dc.type	Conference Proceeding
utslib.location	New York, NY, USA	en_US
utslib.location.activity	Austin, Texas, USA	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Austin, Texas, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	New York, NY, USA	en_US
pubs.start-date	2012-05-05	en_US

Abstract:

There has been an increasing number of quantitative methods to measure and evaluate player experiences. However, current methods either focus on telemetry approaches, which are insucient to capture real life responses, or psychophysiological methods, which are intrusive and more suited to controlled laboratory environments. This paper presents the position that computer vision techniques can provide a less intrusive and more versatile solution for automatic evaluation of game user experiences. A conceptual framework to automatically infer flow intensity is presented and a work-in-progress study is included to demonstrate the feasibility of this research direction.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22972