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	<title>track-and-field jumping &#8211; BIOENGINEER.ORG</title>
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		<title>Visual Transformer Algorithm Scores Athletic Movement from Ordinary RGB Video</title>
		<link>https://bioengineer.org/visual-transformer-algorithm-scores-athletic-movement-from-ordinary-rgb-video/</link>
		
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		<pubDate>Sat, 24 Oct 2026 07:58:37 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[action recognition]]></category>
		<category><![CDATA[gymnastics]]></category>
		<category><![CDATA[joint optimization]]></category>
		<category><![CDATA[local detail attention]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[movement quality assessment]]></category>
		<category><![CDATA[racquet sports]]></category>
		<category><![CDATA[RGB video]]></category>
		<category><![CDATA[sports motion scoring]]></category>
		<category><![CDATA[temporal dependency enhancement]]></category>
		<category><![CDATA[track-and-field jumping]]></category>
		<category><![CDATA[vision transformer]]></category>
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					<description><![CDATA[A Visual Transformer-based algorithm reported in Discover Artificial Intelligence assigns technical scores and grades to gymnastics, jumping, and racquet-sport movements from ordinary RGB video, achieving an RMSE of 3.49, a Grade Accuracy of 84.0 percent, and a Spearman correlation of 0.908 on a held-out test set of 316 videos.]]></description>
		
		
		
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