Fingerstroke time estimates for touchscreen-based mobile gaming interaction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Ahreum | - |
dc.contributor.author | Song, Kiburm | - |
dc.contributor.author | Ryu, Hokyoung Blake | - |
dc.contributor.author | Kim, Jieun | - |
dc.contributor.author | Kwon, Gyuhyun | - |
dc.date.accessioned | 2022-07-15T19:58:52Z | - |
dc.date.available | 2022-07-15T19:58:52Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2015-12 | - |
dc.identifier.issn | 0167-9457 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/155709 | - |
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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | Fingerstroke time estimates for touchscreen-based mobile gaming interaction | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ryu, Hokyoung Blake | - |
dc.contributor.affiliatedAuthor | Kim, Jieun | - |
dc.contributor.affiliatedAuthor | Kwon, Gyuhyun | - |
dc.identifier.doi | 10.1016/j.humov.2015.09.003 | - |
dc.identifier.scopusid | 2-s2.0-84942246021 | - |
dc.identifier.wosid | 000365361700021 | - |
dc.identifier.bibliographicCitation | HUMAN MOVEMENT SCIENCE, v.44, pp.211 - 224 | - |
dc.relation.isPartOf | HUMAN MOVEMENT SCIENCE | - |
dc.citation.title | HUMAN MOVEMENT SCIENCE | - |
dc.citation.volume | 44 | - |
dc.citation.startPage | 211 | - |
dc.citation.endPage | 224 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalResearchArea | Sport Sciences | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.relation.journalWebOfScienceCategory | Psychology | - |
dc.relation.journalWebOfScienceCategory | Psychology, Experimental | - |
dc.relation.journalWebOfScienceCategory | Sport Sciences | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | ENTRY | - |
dc.subject.keywordPlus | NAVIGATION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordAuthor | Mobile game | - |
dc.subject.keywordAuthor | Fingerstroke-level model (FLM) | - |
dc.subject.keywordAuthor | Regression model | - |
dc.subject.keywordAuthor | Finger movement | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0167945715300373?via%3Dihub | - |
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