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

## TL;DR

This paper introduces a framework for personalized sleep analysis on edge devices using synthetic data and model distillation to overcome data and hardware limitations.

## Contribution

A novel framework combining synthetic data generation and profile-aided model distillation for efficient, personalized sleep analysis on edge devices.

## Key findings

- Synthetic data generation improves model training despite limited real-world physiological data.
- Distilled models perform comparably to large language models while meeting edge device constraints.
- The framework enables real-time, personalized sleep analysis on resource-limited hardware.

## Abstract

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.

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.

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.

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.