ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality
Yonchanok Khaokaew Thuc Hanh Nguyen Kaixin Ji Hiruni Kegalle Marwah Alaofi
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.