Four-joint motion data based posture classification for immersive postural correction system
In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can ju...
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Veröffentlicht in: | Multimedia tools and applications 2017-05, Vol.76 (9), p.11235-11249 |
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creator | Ko, Kyeong-Ri Chae, Seung-Hoon Moon, Daesung Seo, Chang Ho Pan, Sung Bum |
description | In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this paper, we propose a four joint-based motion capture system for building immersive postural correction system. The system collects the subject’s postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints’ rotation angles and positions; the normal posture judgment reached a success rate of 99.79 %. This result suggests that the features of the four joints can be used to judge and help correct a user’s posture through application to a spinal disease prevention system in the future. |
doi_str_mv | 10.1007/s11042-016-3299-0 |
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Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this paper, we propose a four joint-based motion capture system for building immersive postural correction system. The system collects the subject’s postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints’ rotation angles and positions; the normal posture judgment reached a success rate of 99.79 %. This result suggests that the features of the four joints can be used to judge and help correct a user’s posture through application to a spinal disease prevention system in the future.</description><identifier>ISSN: 1380-7501</identifier><identifier>EISSN: 1573-7721</identifier><identifier>DOI: 10.1007/s11042-016-3299-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Cluster analysis ; Clustering ; Computer Communication Networks ; Computer Science ; Data collection ; Data Structures and Information Theory ; Feature extraction ; Human body ; Machine learning ; Motion capture ; Multimedia Information Systems ; Posture ; Special Purpose and Application-Based Systems ; Vector quantization</subject><ispartof>Multimedia tools and applications, 2017-05, Vol.76 (9), p.11235-11249</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>Multimedia Tools and Applications is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-398d92f66e087eb90400b52fa79d53f5e9de68d7d63cf0d0df847a9cfd64b42d3</citedby><cites>FETCH-LOGICAL-c316t-398d92f66e087eb90400b52fa79d53f5e9de68d7d63cf0d0df847a9cfd64b42d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11042-016-3299-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11042-016-3299-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Ko, Kyeong-Ri</creatorcontrib><creatorcontrib>Chae, Seung-Hoon</creatorcontrib><creatorcontrib>Moon, Daesung</creatorcontrib><creatorcontrib>Seo, Chang Ho</creatorcontrib><creatorcontrib>Pan, Sung Bum</creatorcontrib><title>Four-joint motion data based posture classification for immersive postural correction system</title><title>Multimedia tools and applications</title><addtitle>Multimed Tools Appl</addtitle><description>In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. 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Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this paper, we propose a four joint-based motion capture system for building immersive postural correction system. The system collects the subject’s postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints’ rotation angles and positions; the normal posture judgment reached a success rate of 99.79 %. 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subjects | Algorithms Cluster analysis Clustering Computer Communication Networks Computer Science Data collection Data Structures and Information Theory Feature extraction Human body Machine learning Motion capture Multimedia Information Systems Posture Special Purpose and Application-Based Systems Vector quantization |
title | Four-joint motion data based posture classification for immersive postural correction system |
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