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dc.contributor.authorChen, Hua-Tsungen_US
dc.contributor.authorHe, Yu-Zhenen_US
dc.contributor.authorChou, Chien-Lien_US
dc.contributor.authorLee, Suh-Yinen_US
dc.contributor.authorLin, Bao-Shuh P.en_US
dc.contributor.authorYu, Jen-Yuen_US
dc.date.accessioned2014-12-08T15:35:40Z-
dc.date.available2014-12-08T15:35:40Z-
dc.date.issued2013en_US
dc.identifier.issn2330-7927en_US
dc.identifier.urihttp://hdl.handle.net/11536/24091-
dc.description.abstractSelf-training plays an important role in sports exercise. However, if not under the instruction of a coach, improper training postures can 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, entitled YogaST, which aims at instructing the user/practitioner to perform the asana (Yoga posture) correctly and preventing injury caused by improper postures. Involving professional Yoga training knowledge, YogaST analyzes the practitioner's posture from both front and side views using two Kinects with perpendicular viewing directions and assists him/her in rectifying bad postures. The contour, skeleton, and feature axes of the human body are extracted as posture representation. Then, YogaST analyzes the practitioner's posture and presents visualized instruction for posture rectification so that the practitioner can easily understand how to adjust his/her posture.en_US
dc.language.isoen_USen_US
dc.subjectSports trainingen_US
dc.subjectself-learningen_US
dc.subjectposture analysisen_US
dc.subjectKinecten_US
dc.subjectsports video analysisen_US
dc.titleCOMPUTER-ASSISTED SELF-TRAINING SYSTEM FOR SPORTS EXERCISE USING KINECTSen_US
dc.typeProceedings Paperen_US
dc.identifier.journalELECTRONIC PROCEEDINGS OF THE 2013 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO WORKSHOPS (ICMEW)en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000335245800092-
Appears in Collections:Conferences Paper