Pathological gait clustering in post-stroke patients using motion capture data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Kim, Y.-H. | - |
dc.contributor.author | Kim, S.-J. | - |
dc.contributor.author | Choi, M.-T. | - |
dc.date.accessioned | 2022-04-28T23:40:19Z | - |
dc.date.available | 2022-04-28T23:40:19Z | - |
dc.date.created | 2022-04-28 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 0966-6362 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/140495 | - |
dc.description.abstract | Background: Analyzing the complex gait patterns of post-stroke patients with lower limb paralysis is essential for rehabilitation. Research question: Is it feasible to use the full joint-level kinematic features extracted from the motion capture data of patients directly to identify the optimal gait types that ensure high classification performance? Methods: In this study, kinematic features were extracted from 111 gait cycle data on joint angles, and angular velocities of 36 post-stroke patients were collected eight times over six months using a motion capture system. Simultaneous clustering and classification were applied to determine the optimal gait types for reliable classification performance. Results: In the given dataset, six optimal gait groups were identified, and the clustering and classification performances were denoted by a silhouette coefficient of 0.1447 and F1 score of 1.0000, respectively. Significance: There is no distinct clinical classification of post-stroke hemiplegic gaits. However, in contrast to previous studies, more optimal gait types with a high classification performance fully utilizing the kinematic features were identified in this study. © 2022 | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Pathological gait clustering in post-stroke patients using motion capture data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, S.-J. | - |
dc.identifier.doi | 10.1016/j.gaitpost.2022.03.007 | - |
dc.identifier.scopusid | 2-s2.0-85127303225 | - |
dc.identifier.wosid | 000806791900008 | - |
dc.identifier.bibliographicCitation | Gait and Posture, v.94, pp.210 - 216 | - |
dc.relation.isPartOf | Gait and Posture | - |
dc.citation.title | Gait and Posture | - |
dc.citation.volume | 94 | - |
dc.citation.startPage | 210 | - |
dc.citation.endPage | 216 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalResearchArea | Orthopedics | - |
dc.relation.journalResearchArea | Sport Sciences | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.relation.journalWebOfScienceCategory | Orthopedics | - |
dc.relation.journalWebOfScienceCategory | Sport Sciences | - |
dc.subject.keywordPlus | STROKE PATIENTS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | PATTERNS | - |
dc.subject.keywordAuthor | Gait kinematic features | - |
dc.subject.keywordAuthor | Gait patterns | - |
dc.subject.keywordAuthor | Hemiplegia | - |
dc.subject.keywordAuthor | Post-stroke | - |
dc.subject.keywordAuthor | Simultaneous clustering and classification | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.