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Cluster Dominance Analysis of Strength Training Motion Characteristics
This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each clust...
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| creator | Toba, Hapnes Wianto, Elizabeth Malinda, Maya Al Halim, Agung Wijaya Chen, Chien-Hsu |
| description | This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches. |
| doi_str_mv | 10.1109/GCCE59613.2023.10315252 |
| format | conference_proceeding |
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The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.</description><identifier>EISSN: 2693-0854</identifier><identifier>EISBN: 9798350340181</identifier><identifier>DOI: 10.1109/GCCE59613.2023.10315252</identifier><language>eng</language><publisher>IEEE</publisher><subject>cluster analysis ; Consumer electronics ; Data collection ; density affinity ; Electric potential ; Mobile handsets ; motion patterns ; smartwatch ; strength training motion ; Training</subject><ispartof>2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), 2023, p.1125-1128</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10315252$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27916,54546,54923</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10315252$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Toba, Hapnes</creatorcontrib><creatorcontrib>Wianto, Elizabeth</creatorcontrib><creatorcontrib>Malinda, Maya</creatorcontrib><creatorcontrib>Al Halim, Agung Wijaya</creatorcontrib><creatorcontrib>Chen, Chien-Hsu</creatorcontrib><title>Cluster Dominance Analysis of Strength Training Motion Characteristics</title><title>2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)</title><addtitle>GCCE</addtitle><description>This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. 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The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.</description><subject>cluster analysis</subject><subject>Consumer electronics</subject><subject>Data collection</subject><subject>density affinity</subject><subject>Electric potential</subject><subject>Mobile handsets</subject><subject>motion patterns</subject><subject>smartwatch</subject><subject>strength training motion</subject><subject>Training</subject><issn>2693-0854</issn><isbn>9798350340181</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1j8FOAyEURdHExKbOH5jID8z4gIGBZTO21aTGhXXdMMOjxbSMAVz0722iru7qnJxLyAODhjEwj-u-X0qjmGg4cNEwEExyya9IZTqjhQTRAtPsmsy4MqIGLdtbUuX8CQBcAjeKz8iqP37ngok-TacQbRyRLqI9nnPIdPL0vSSM-3Kg22RDDHFPX6cSpkj7g012vIAhlzDmO3Lj7TFj9bdz8rFabvvnevO2fukXmzowZkrN0KFWiB1ab3BAMYAGPepBOq_V0ErvWs5GZ5zopL9ECqW9ar2AjrvOKTEn97_egIi7rxRONp13_9fFD4cXToI</recordid><startdate>20231010</startdate><enddate>20231010</enddate><creator>Toba, Hapnes</creator><creator>Wianto, Elizabeth</creator><creator>Malinda, Maya</creator><creator>Al Halim, Agung Wijaya</creator><creator>Chen, Chien-Hsu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20231010</creationdate><title>Cluster Dominance Analysis of Strength Training Motion Characteristics</title><author>Toba, Hapnes ; Wianto, Elizabeth ; Malinda, Maya ; Al Halim, Agung Wijaya ; Chen, Chien-Hsu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i119t-1ede86ee7eaf9ebe3b0808c8b5df86b45fd421cd9d375f250368f64f3072d7d63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>cluster analysis</topic><topic>Consumer electronics</topic><topic>Data collection</topic><topic>density affinity</topic><topic>Electric potential</topic><topic>Mobile handsets</topic><topic>motion patterns</topic><topic>smartwatch</topic><topic>strength training motion</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Toba, Hapnes</creatorcontrib><creatorcontrib>Wianto, Elizabeth</creatorcontrib><creatorcontrib>Malinda, Maya</creatorcontrib><creatorcontrib>Al Halim, Agung Wijaya</creatorcontrib><creatorcontrib>Chen, Chien-Hsu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Toba, Hapnes</au><au>Wianto, Elizabeth</au><au>Malinda, Maya</au><au>Al Halim, Agung Wijaya</au><au>Chen, Chien-Hsu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cluster Dominance Analysis of Strength Training Motion Characteristics</atitle><btitle>2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)</btitle><stitle>GCCE</stitle><date>2023-10-10</date><risdate>2023</risdate><spage>1125</spage><epage>1128</epage><pages>1125-1128</pages><eissn>2693-0854</eissn><eisbn>9798350340181</eisbn><abstract>This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.</abstract><pub>IEEE</pub><doi>10.1109/GCCE59613.2023.10315252</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2693-0854 |
| ispartof | 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE), 2023, p.1125-1128 |
| issn | 2693-0854 |
| language | eng |
| recordid | cdi_ieee_primary_10315252 |
| source | IEEE Xplore All Conference Series |
| subjects | cluster analysis Consumer electronics Data collection density affinity Electric potential Mobile handsets motion patterns smartwatch strength training motion Training |
| title | Cluster Dominance Analysis of Strength Training Motion Characteristics |
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