An Interactive GPT Approach to Enhance Sleep Quality

Abstract

In today’s world, sleep quality is pivotal for overall well-being. While wearable sensors offer real-time monitoring, they often lack actionable insights, leading to user abandonment. This paper delves into the role of technology in understanding sleep patterns. We introduce a two-stage framework, utilizing Large Language Models (LLMs), aiming to provide accurate sleep predictions with actionable feedback. Leveraging the GLOBEM dataset and synthetic data from LLMs, we highlight enhanced results with models like XGBoost. Our approach merges advanced machine learning with user-centric design, blending scientific accuracy with practicality.

Citation

If you find this paper useful, please cite it using the following BibTeX:

@INPROCEEDINGS{khaokaew2023zzzgpt,
      title={ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality}, 
      author={Yonchanok Khaokaew and Thuc Hanh Nguyen and Kaixin Ji and Hiruni Kegalle and Marwah Alaofi},
      year={2023},
      eprint={2310.16242},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}