I-Keyboard: Fully Imaginary Keyboard on Touch Devices Empowered by Deep Neural Decoder

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dc.contributor.authorKim, Ue-Hwanko
dc.contributor.authorYoo, Sang-Minko
dc.contributor.authorKim, Jong-Hwanko
dc.date.accessioned2021-10-18T04:50:09Z-
dc.date.available2021-10-18T04:50:09Z-
dc.date.created2020-01-14-
dc.date.created2020-01-14-
dc.date.created2020-01-14-
dc.date.issued2021-09-
dc.identifier.citationIEEE TRANSACTIONS ON CYBERNETICS, v.51, no.9, pp.4528 - 4539-
dc.identifier.issn2168-2267-
dc.identifier.urihttp://hdl.handle.net/10203/288208-
dc.description.abstractText-entry aims to provide an effective and efficient pathway for humans to deliver their messages to computers. With the advent of mobile computing, the recent focus of text-entry research has moved from physical keyboards to soft keyboards. Current soft keyboards, however, increase the typo rate due to lack of tactile feedback and degrade the usability of mobile devices due to their large portion on screens. To tackle these limitations, we propose a fully imaginary keyboard (I-Keyboard) with a deep neural decoder (DND). The invisibility of I-Keyboard maximizes the usability of mobile devices and DND empowered by a deep neural architecture allows users to start typing from any position on the touch screens at any angle. To the best of our knowledge, the eyes-free ten-finger typing scenario of I-Keyboard which does not necessitate both a calibration step and a predefined region for typing is first explored in this work. For the purpose of training DND, we collected the largest user data in the process of developing I-Keyboard. We verified the performance of the proposed I-Keyboard and DND by conducting a series of comprehensive simulations and experiments under various conditions. I-Keyboard showed 18.95% and 4.06% increases in typing speed (45.57 WPM) and accuracy (95.84%), respectively over the baseline.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleI-Keyboard: Fully Imaginary Keyboard on Touch Devices Empowered by Deep Neural Decoder-
dc.typeArticle-
dc.identifier.wosid000696078900019-
dc.identifier.scopusid2-s2.0-85115172032-
dc.type.rimsART-
dc.citation.volume51-
dc.citation.issue9-
dc.citation.beginningpage4528-
dc.citation.endingpage4539-
dc.citation.publicationnameIEEE TRANSACTIONS ON CYBERNETICS-
dc.identifier.doi10.1109/TCYB.2019.2952391-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorKim, Jong-Hwan-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorKeyboards-
dc.subject.keywordAuthorDecoding-
dc.subject.keywordAuthorMobile handsets-
dc.subject.keywordAuthorCalibration-
dc.subject.keywordAuthorData collection-
dc.subject.keywordAuthorTactile sensors-
dc.subject.keywordAuthorUsability-
dc.subject.keywordAuthorDecoding-
dc.subject.keywordAuthoreyes-free-
dc.subject.keywordAuthorhuman-computer interaction (HCI)-
dc.subject.keywordAuthorimaginary keyboard (I-Keyboard)-
dc.subject.keywordAuthorsoft keyboard-
dc.subject.keywordAuthortext entry-
dc.subject.keywordAuthoruser experience-
dc.subject.keywordAuthoruser interfaces (UIs)-
dc.subject.keywordAuthorvirtual keyboard-
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