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	<title>self-reported wellness &#8211; BIOENGINEER.ORG</title>
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		<title>When Numbers Meet Words: Hybrid Data Representations Push Wearable Wellness AI Forward</title>
		<link>https://bioengineer.org/when-numbers-meet-words-hybrid-data-representations-push-wearable-wellness-ai-forward/</link>
		
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		<pubDate>Sat, 17 Oct 2026 12:29:36 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[fine-tuning]]></category>
		<category><![CDATA[GPT-4o-mini]]></category>
		<category><![CDATA[health informatics]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[LoRA]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[PMData dataset]]></category>
		<category><![CDATA[representation learning]]></category>
		<category><![CDATA[self-reported wellness]]></category>
		<category><![CDATA[wearable devices]]></category>
		<category><![CDATA[WellFusion]]></category>
		<category><![CDATA[wellness prediction]]></category>
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					<description><![CDATA[A new study shows that how wearable sensor data is translated into language-model inputs—raw numbers, category labels, or both—critically shapes multi-target wellness prediction, with hybrid representations showing descriptive advantages but conventional baselines and small-sample constraints tempering the hype.]]></description>
		
		
		
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