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dc.contributor.authorLee, Jung Ho-
dc.contributor.authorShin, Beomju-
dc.contributor.authorLee, Seok-
dc.contributor.authorPark, Jinwoo-
dc.contributor.authorKim, Chulki-
dc.contributor.authorLee, Taikjin-
dc.contributor.authorKim, Jae Hun-
dc.date.accessioned2024-01-19T12:07:52Z-
dc.date.available2024-01-19T12:07:52Z-
dc.date.created2022-03-01-
dc.date.issued2014-06-
dc.identifier.issn2158-3994-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/115346-
dc.description.abstractThis paper presents a motion recognition based step length estimation algorithm using smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of linear combination based step length model are adapted based on the result motion recognition. In order to verify the proposed algorithm, we performed experiments on 5 subjects and showed accuracy of step length estimation as RMSE.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleMotion based Adaptive Step Length Estimation using Smartphone-
dc.typeConference-
dc.description.journalClass1-
dc.identifier.bibliographicCitation18th IEEE International Symposium on Consumer Electronics (ISCE)-
dc.citation.title18th IEEE International Symposium on Consumer Electronics (ISCE)-
dc.citation.conferencePlaceUS-
dc.citation.conferencePlaceSOUTH KOREA-
dc.citation.conferenceDate2014-06-22-
dc.relation.isPartOf18TH IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS (ISCE 2014)-
dc.identifier.wosid000361020200178-
dc.identifier.scopusid2-s2.0-84907385027-
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KIST Conference Paper > 2014
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