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This paper proposes an interpretable causal-discovery-guided framework for deriving a Sleep Recovery Score (SRS) from multimodal polysomnography data, demonstrating up to 2.5× stronger alignment with perceived recovery than the traditional Apnea–Hypopnea Index (AHI), with potential applications in connected health.
This paper proposes the Personal Care Utility (PCU), an event-driven architecture designed to bridge the gap between episodic clinical care and the 8,759 hours of daily life that shape health. It organizes continuous personal data into meaningful events, uses LLMs for reasoning and communication while grounding clinical decisions in evidence, and instantiates the framework for Type 2 Diabetes management.
AnyMo is a geometry-aware framework for setup-agnostic human motion modeling using physics-grounded IMU simulation and graph encoding, achieving significant improvements in zero-shot activity recognition, cross-modal retrieval, and motion captioning across multiple datasets.