Matches in UGent Biblio for { <https://biblio.ugent.be/publication/1170816#aggregation> ?p ?o. }
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- aggregation classification "C1".
- aggregation creator B72491.
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- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 1170816.bibtex.
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- aggregation isPartOf urn:isbn:9781424492299.
- aggregation isPartOf urn:isbn:9781424492305.
- aggregation isPartOf urn:issn:2156-9711.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Social Sciences".
- aggregation title "QoE estimation of a location-based mobile game using on-body sensors and QoS-related data".
- aggregation abstract "The scope of this paper is the interdisciplinary measurement and modeling methodology of Quality of Experience (QoE) when playing a mobile location-based massively multiplayer online role-playing game (MMORPG) that places the virtual world on top of the real world using the user's location. The paper introduces the implementation of a re-usable mobile QoE measurement framework on the Android platform and illustrates how it was applied in a concrete case-study. In this respect, a multidimensional QoE prediction model consisting of both objective and subjective parameters is presented. In this model, test users' evaluations regarding feelings of amusement, absorption or engagement experienced while playing a popular location based mobile game (Parallel Kingdom) are taken into account and related to a set of objective QoS-related parameters, contextual data, and physiological data obtained from an on-body sensor. The latter evidences the intensity of physical activity of the test users during the gaming session, taking into account Metabolic Equivalent of Task (MET) measurements. Results of this study indicate that the test users' QoE was influenced by their physical effort, the data connection type used, and the playing context (e.g. interaction with other players). Despite of the fact of using a re-usable framework for QoE estimation, the analysis is limited to this particular popular (more than 100000 users) mobile game.".
- aggregation authorList BK184635.
- aggregation endPage "5".
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- aggregation aggregates 1170830.
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- aggregation similarTo WD.2010.5657756.
- aggregation similarTo LU-1170816.