Abstract
The integration of smart wearable and home health monitoring devices marks a new epoch in health management, offering deep insights through real-time data. This study seeks to convert this collected health data into actionable personalized assessments and recommendations, enhancing health management efficacy. Utilizing literature review, case analysis, and data analysis, the research initially describes smart devices’ capabilities and data gathering techniques. It then concentrates on data processing methodologies, statistical analysis, and machine learning, examining the translation of these into usable health insights. The study reveals that smart devices offer continuous health data, enabling the identification of individual trends and risks through data analysis. Personalized assessments can boost engagement and adherence to health plans. Yet, it acknowledges challenges in data privacy, technological synthesis, and user adoption, proposing solutions like fortified security, interface optimization, and enhanced analytical accuracy. In conclusion, the research highlights the potential of data analysis in advancing personalized health management and recommends future explorations, such as refining algorithms, bolstering privacy safeguards, and setting regulatory benchmarks.
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