Exploration of AI-Enhanced Wearable Devices for Advanced Cardiovascular Monitoring in the Elderly

In this paper, an approach to remote health monitoring is explored, focusing on implementing predictive algorithms. At the heart of our exploration is the goal to meet the varied needs of patients, from treatment to prevention and empower them to network and share their health data securely with their caregivers, all within an individual-centered approach. The methodology hinges on designing functionalities that extract meaningful insights from data provided by advanced sensor technologies. This data, meticulously organized into a hierarchical structure, transitions from basic measurements like heart rate and oxygenation levels to comprehensive health assessments. Such a scheme allows for a nuanced understanding of patient health, considering distinct pathologies, specifically cardiovascular, in this case study. Central are advanced reasoning algorithms, particularly Bayesian Networks and Decision Trees. These frameworks have been used to abstract sensor information into higher semantic levels, transforming raw data into actionable insights about daily conditions. For instance, through simulated data, these algorithms undergo rigorous testing, refining their predictive capacities in a controlled environment before their application in field tests. This stratagem not only maximizes the potential for enhanced patient monitoring through a remotely governed technological platform but also aims at streamlining healthcare systems. Through this, we put the foundation for future applications with real patient data that will drive personalized, predictive healthcare solutions.