Personalized Activity Recommendation System for Cardiovascular Patients Using Heart Rate and ECG/EKG Data from Wearable Devices

Worldwide, cardiovascular diseases such as heart disease, hypertension, and high blood pressure are leading causes of death. These patients need proper management of physical activity because it can be favorable to health outcome and prevent complications. In this research, we present a personalized activity recommendation system for individuals with cardiovascular conditions. It utilizes real-time heart rate and ECG/EKG data from wearable devices, such as smartwatches and fitness monitors. The proposed system analyzes these biometric signals to provide tailored activity recommendations that are safe and consistent with the user's existing heart health. To keep the algorithms in constant adaptation to the physiological data, patterns written in machine learning algorithms are used to analyze and create continuous changes in recommendations depending on user conditions and trends. The system is designed to help patients stay active and minimize health risks while taking into account both real-time and historical health data. The approach improves the effectiveness of remote health monitoring and enables personalized and preventative healthcare for cardiovascular patients.