# PDSRS-LD: Personalized Deep Learning-Based Sleep Recommendation System Using Lifelog Data

## TL;DR

This paper introduces a personalized sleep recommendation system that uses lifelog data and deep learning to improve sleep quality based on daily activities and user profiles.

## Contribution

The novelty lies in integrating lifelog data with deep learning for personalized sleep recommendations, outperforming existing models.

## Key findings

- PDSRS-LD outperforms existing models in F1 score and Average Precision (mAP).
- The system effectively analyzes relationships between sleep quality, stress, and fatigue.
- It shows potential for real-time, user-centric sleep management in smart healthcare systems.

## Abstract

This study proposes a Personalized Deep Learning-Based Sleep Recommendation System Using Lifelog Data (PDSRS-LD). Traditional sleep research primarily relies on bio signals such as EEG and ECG recorded during sleep but often fails to sufficiently reflect the influence of daily activities on sleep quality. To address this limitation, we collect lifelog data such as stress levels, fatigue, and sleep satisfaction via wearable devices and use them to construct individual user profiles. Subsequently, real sleep data obtained from an AI-powered motion bed are incorporated for secondary training to enhance recommendation performance. PDSRS-LD considers comprehensive user data, including gender, age, and physical activity, to analyze the relationships among sleep quality, stress, and fatigue. Based on this analysis, the system provides personalized sleep improvement strategies. Experimental results demonstrate that the proposed system outperforms existing models in terms of F1 score and Average Precision (mAP). These results suggest that PDSRS-LD is effective for real-time, user-centric sleep management and holds significant potential for integration into future smart healthcare systems.