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Cited 8 time in webofscience Cited 10 time in scopus
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dc.contributor.authorLee, H-
dc.contributor.authorHan, G-
dc.contributor.authorLee, I-
dc.contributor.authorYim, S-
dc.contributor.authorHong, K-
dc.contributor.authorLee, H-
dc.contributor.authorChoi, S-
dc.date.accessioned2016-03-31T08:00:39Z-
dc.date.available2016-03-31T08:00:39Z-
dc.date.created2014-10-07-
dc.date.issued2013-11-
dc.identifier.issn2168-2291-
dc.identifier.other2014-OAK-0000030375-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/14360-
dc.description.abstractThis paper investigates the effect of haptic feedback on the learning of a 2-D sequential selection task, used as an abstraction of complex industrialmanual assembly tasks. This mnemonic-motor task requires memorization of the selection order of points scattered on a 2-D plane and reproduction of this order using entire arm movements. Four information presentation methods, visual information only, visual information + enactment, visual information + haptic guidance, and visual information + haptic disturbance, are considered. The latter three methods provide different levels of haptic kinesthetic feedback to the trainee. We carried out a user study to assess the quantitative performance differences of the four training methods using a custom-built visuo-haptic training system. Experimental results showed the relative advantages and disadvantages of each information presentation method for both short-term and long-term memorization. In particular, training with only visual information was the best option for short-term memory, while training also with haptic disturbance was the most effective for long-term memory. Our findings have implications to designing a training method that is suitable for given training requirements.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.relation.isPartOfIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS-
dc.titleHaptic Assistance for Memorization of 2-D Selection Sequences-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1109/TSMC.2013.2283464-
dc.author.googleLee, H-
dc.author.googleHan, G-
dc.author.googleLee, I-
dc.author.googleYim, S-
dc.author.googleHong, K-
dc.author.googleChoi, S-
dc.relation.volume43-
dc.relation.issue6-
dc.relation.startpage643-
dc.relation.lastpage649-
dc.contributor.id10127373-
dc.relation.journalIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, v.43, no.6, pp.643 - 649-
dc.identifier.wosid000327969300012-
dc.date.tcdate2019-01-01-
dc.citation.endPage649-
dc.citation.number6-
dc.citation.startPage643-
dc.citation.titleIEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS-
dc.citation.volume43-
dc.contributor.affiliatedAuthorLee, H-
dc.contributor.affiliatedAuthorChoi, S-
dc.identifier.scopusid2-s2.0-84893065028-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc2-
dc.description.scptc4*
dc.date.scptcdate2018-05-121*
dc.description.isOpenAccessN-
dc.type.docTypeArticle-
dc.subject.keywordPlusRETRIEVAL-PROCESSES-
dc.subject.keywordPlusATTENTION-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusPOWER-
dc.subject.keywordPlusTASK-
dc.subject.keywordAuthorAssembly-
dc.subject.keywordAuthorassistance technology-
dc.subject.keywordAuthor2-D selection sequence-
dc.subject.keywordAuthorenactment-
dc.subject.keywordAuthorhaptic disturbance-
dc.subject.keywordAuthorhaptic guidance-
dc.subject.keywordAuthorhaptics-
dc.subject.keywordAuthormemory-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Cybernetics-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

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