GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ju, Chan-Yang | - |
dc.contributor.author | Kim, Jong-Hyeon | - |
dc.contributor.author | Lee, Dong-Ho | - |
dc.date.accessioned | 2023-11-24T02:31:22Z | - |
dc.date.available | 2023-11-24T02:31:22Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115658 | - |
dc.description.abstract | Digital fitness has become a widely used tool for remote exercise guidance, leveraging artificial intelligence to analyze exercise videos and support self-training. This paper introduces a method for self-training in golf, a sport where automated posture analysis can significantly reduce the costs associated with professional coaching. Our system utilizes a pose refinement methodology and an explainable golf swing embedding for analyzing the swing motions of learners and professional golfers. By leveraging sequential coordinate information, we detect biased pose joints and refine the 2D and 3D human pose estimation results. Furthermore, we propose a swing embedding method that considers geometric information extracted from the swing pose. This approach enables not only the comparison of the similarity between two golf swing poses but also the visualization of different points, providing learners with specific and intuitive feedback on areas that require correction. Our experimental results demonstrate the effectiveness of our swing guide system in identifying specific body points that need adjustment to align more closely with a professional golfer's swing. This research contributes to the digital fitness domain by enhancing the accuracy of posture analysis and providing a specialized and interpretable golf swing analysis system. Our proposed system offers a low-cost and time-efficient approach for users who wish to improve their golf swing, paving the way for broader applications of digital fitness technologies in self-training contexts. | - |
dc.format.extent | 20 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/app132011227 | - |
dc.identifier.scopusid | 2-s2.0-85192481551 | - |
dc.identifier.wosid | 001090627100001 | - |
dc.identifier.bibliographicCitation | Applied Sciences-basel, v.13, no.20, pp 1 - 20 | - |
dc.citation.title | Applied Sciences-basel | - |
dc.citation.volume | 13 | - |
dc.citation.number | 20 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 20 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | digital fitness | - |
dc.subject.keywordAuthor | self-training | - |
dc.subject.keywordAuthor | human pose estimation | - |
dc.subject.keywordAuthor | explainable representation | - |
dc.subject.keywordAuthor | golf swing analysis | - |
dc.identifier.url | https://www.mdpi.com/2076-3417/13/20/11227 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.