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		<title>AI Model Reads Anxiety From Wearable Signals and Suggests Ways to Calm Down</title>
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		<pubDate>Mon, 05 Oct 2026 23:53:30 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[affective computing]]></category>
		<category><![CDATA[anxiety quantification]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[digital mental health]]></category>
		<category><![CDATA[Emotion regulation]]></category>
		<category><![CDATA[hyperbolic representation learning]]></category>
		<category><![CDATA[neuro-symbolic reasoning]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>
		<category><![CDATA[stochastic differential equations]]></category>
		<category><![CDATA[uncertainty-aware modeling]]></category>
		<category><![CDATA[wearable sensors]]></category>
		<category><![CDATA[WESAD dataset]]></category>
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					<description><![CDATA[A new deep learning framework quantifies anxiety-related physiological arousal from wearable biosignals in continuous time and generates interpretable, constraint-based emotional regulation recommendations.]]></description>
		
		
		
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