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dc.contributor.authorChen, Hua-Tsungen_US
dc.contributor.authorHe, Yu-Zhenen_US
dc.contributor.authorHsu, Chun-Chiehen_US
dc.date.accessioned2019-04-02T05:58:37Z-
dc.date.available2019-04-02T05:58:37Z-
dc.date.issued2018-09-01en_US
dc.identifier.issn1380-7501en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11042-018-5721-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/148015-
dc.description.abstractSelf-training is essential in sports exercise. However, without the instruction of a coach, a practitioner may progress to a limited extent. Improper postures may even cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a yoga self-training system, which aims at instructing the practitioner to perform yoga poses correctly, assisting in rectifying poor postures, and preventing injury. Integrating computer vision techniques, the proposed system analyzes the practitioner's posture from both front and side views by extracting the body contour, skeleton, dominant axes, and feature points. Then, based on the domain knowledge of yoga training, visualized instructions for posture rectification are presented so that the practitioner can easily understand how to adjust his/her posture. Experiments on twelve yoga poses performed by different practitioners validate the feasibility of the proposed system in yoga training.en_US
dc.language.isoen_USen_US
dc.subjectSports trainingen_US
dc.subjectMultimedia systemen_US
dc.subjectComputer visionen_US
dc.subjectImage processingen_US
dc.subjectYogaen_US
dc.subjectPosture analysisen_US
dc.subjectBody skeletonen_US
dc.titleComputer-assisted yoga training systemen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11042-018-5721-2en_US
dc.identifier.journalMULTIMEDIA TOOLS AND APPLICATIONSen_US
dc.citation.volume77en_US
dc.citation.spage23969en_US
dc.citation.epage23991en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000441760900038en_US
dc.citation.woscount1en_US
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