Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권규현 | - |
dc.date.accessioned | 2017-10-17T04:49:02Z | - |
dc.date.available | 2017-10-17T04:49:02Z | - |
dc.date.issued | 2015-12 | - |
dc.identifier.citation | HUMAN MOVEMENT SCIENCE, v. 44, Page. 211-224 | en_US |
dc.identifier.issn | 0167-9457 | - |
dc.identifier.issn | 1872-7646 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0167945715300373?via%3Dihub | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/30066 | - |
dc.description.abstract | The growing popularity of gaming applications and ever-faster mobile carrier networks have called attention to an intriguing issue that is closely related to command input performance. A challenging mirroring game service, which simultaneously provides game service to both PC and mobile phone users, allows them to play games against each other with very different control interfaces. Thus, for efficient mobile game design, it is essential to apply a new predictive model for measuring how potential touch input compares to the PC interfaces. The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smart-phone interface (i.e., tapping, pointing, dragging, and flicking). A modified KLM, tentatively called the fingerstroke-level model (FLM), is proposed using time estimates on regression models. (C) 2015 Elsevier B.V. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported by the research fund of Hanyang University in South Korea (HY-201400000001033). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER SCIENCE BV | en_US |
dc.subject | Mobile game | en_US |
dc.subject | Fingerstroke-level model (FLM) | en_US |
dc.subject | Regression model | en_US |
dc.subject | Finger movement | en_US |
dc.title | Fingerstroke time estimates for touchscreen-based mobile gaming interaction | en_US |
dc.type | Article | en_US |
dc.relation.volume | 44 | - |
dc.identifier.doi | 10.1016/j.humov.2015.09.003 | - |
dc.relation.page | 211-224 | - |
dc.relation.journal | HUMAN MOVEMENT SCIENCE | - |
dc.contributor.googleauthor | Lee, Ahreum | - |
dc.contributor.googleauthor | Song, Kiburm | - |
dc.contributor.googleauthor | Ryu, Hokyoung Blake | - |
dc.contributor.googleauthor | Kim, Jieun | - |
dc.contributor.googleauthor | Kwon, Gyuhyun | - |
dc.relation.code | 2015015824 | - |
dc.sector.campus | S | - |
dc.sector.daehak | GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S] | - |
dc.sector.department | DEPARTMENT OF TECHNOLOGY MANAGEMENT | - |
dc.identifier.pid | ghkwon | - |
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