Implementing a Health Recommendation System from Wearable Data

Abstract

The rising interest in personalized health monitoring has created a demand for intelligent systems that not only evaluate an individual’s health status but also offer actionable recommendations. This dissertation presents a data-driven approach to assess overall health by calculating a weekly health score using multi-dimensional data sources such as sleep patterns, nutrition, cardiovascular activity, fitness levels, and metabolic parameters. The system integrates and processes data stored in MongoDB using Python, applies scoring logic tailored to each health domain, and aggregates them into a unified health score. Addi- tionally, the system generates a detailed summary and leverages a language model to extract personalized recommendations aimed at improving user well-being. A comprehensive PDF health report is produced, featuring score visualizations and advice tailored to the individual. The implementation was tested across multiple profiles, and evaluation metrics indicate that the approach is both adaptive and insightful. This work not only demonstrates a scalable pipeline for health analysis but also opens up opportunities for future integration of machine learning and deeper behavioral insights.