Dynamic personalized health management through the Health Assistant AI Fusion Framework

Haonan Li, Yongqing Wang, Qisheng Zhang, Samson S. Yu, Fei Wang, Sicheng Chen, Chee Peng Lim

Abstract: As health management increasingly gains importance, the demand for personalized systems is on the rise. Although wearable devices and smartphones generate substantial amounts of data, the effective integration and utilization of this information pose significant challenges. Traditional health management systems often depend on single-technology methodologies and encounter difficulties in processing complex, multi-modal data. These systems typically lack robust integration frameworks and do not adequately address the requirements for personalized health management. To overcome these shortcomings, we propose the Health Assistant AI Fusion Framework (HAAFF), which aims to provide a comprehensive intelligent, and personalized health management solution. HAAFF is comprised of four primary modules: data acquisition, information processing, scene recognition, and generation and interaction. The data acquisition module is responsible for gathering diverse user data, while the information processing module conducts initial data processing. The scene recognition module utilizes sensor data to ascertain user contexts, and the generation and interaction module offers personalized health recommendations based on the analyzed data. To validate the efficacy of HAAFF, we utilize binaural beats as a practical case study, collecting user datasets through Apple Watch and iPhones. The information processing module facilitates preliminary data handling, while the scene recognition module employs machine learning techniques to identify user contexts. Ultimately, the generation and interaction module leverages large language models to produce personalized binaural beat music and provide real-time feedback. The results demonstrate that HAAFF successfully integrates and analyzes multi-source data, generates tailored binaural beat music, and adapts and optimizes based on user feedback, thereby highlighting its potential applications in personalized health management.