Profile-aided distillation framework for personalized sleep analysis with compact models using LLM-guided synthetic data.

Introduction: Enabling personalized sleep analysis and interaction directly on edge devices is crucial for providing real-time health insights and tailored guidance. However, this goal remains challenging due to the scarcity of high-quality physiological data and the computational constraints of edge hardware. Methods: We propose a framework for personalized sleep analysis on edge devices that addresses two key obstacles: limited publicly available physiological datasets and the restricted capacity of compact models. To mitigate data scarcity, we introduce a Physiologically-Constrained Adaptive Hierarchical Copula approach, which leverages large language model-guided optimization to synthesize diverse and realistic physiological signals. To enhance personalized inference on resource-limited models, we further develop Profile-Aided Distillation of Expert Inference with MoE LoRA, which integrates user-specific profile information to improve the performance of edge-deployed models. Results: Extensive experiments on both public and in-house datasets show that the distilled models achieve performance comparable to state-of-the-art large language models, while operating efficiently within the computational and memory constraints of edge devices. Discussion: These results demonstrate that the proposed framework offers a practical and effective solution for enabling personalized sleep analysis and user interaction in resource-constrained environments, bridging the gap between high-performance modeling and real-time, on-device healthcare applications.