Blood Guard

Blood Guard

Blood Guard

Blood Guard

Blood Guard
  • Home
  • Mission
  • Works
  • Team

  • This is
    Blood
    Guard

    Welcome

    Wearable Health Monitoring

    Integrated into wearable technology to track cardiovascular patients' key health metrics daily, like blood pressure and cholesterol

    AI-Powered Insights

    Our machine learning model analyzes patient data, providing insights into their recovery and overall health trends, helping them stay proactive

    User Friendly

    Patients can view real-time progress in our app, making it easy to see their health status and improvement over time.

  • Mission Statement

    BloodGuard aims to empower cardiovascular patients by providing continuous, data-informed insights to support their journey toward better health and recovery through seamless integration of wearable technology, intelligent data analysis, and user-friendly visualization.

    Why Blood Guard?

    Cardiovascular diseases are the leading global cause of death, accounting for 17.9 million deaths annually, which is 32% of all global deaths. By 2035, cardiovascular-related costs in the U.S. alone are projected to reach $1.1 trillion annually due to medical expenses and lost productivity. Many cardiovascular conditions are preventable, yet without proactive health monitoring, individuals miss opportunities for early detection and lifestyle adjustments.

  • How Blood Guard works

    1

    Data Collection and Model Training

    Acquired cardiovascular patient data and pre-processed it using Python (pandas, NumPy) to clean and structure the dataset.
    Trained a machine learning model with scikit-learn to identify patterns in cardiovascular health trends.

    2

    Integration and Deployment

    Deployed the trained model onto a Kotlin-based mobile app connected to a wearable device for real-time data analysis.
    Enabled the app to display personalized health insights and facilitate direct communication with healthcare providers.

    3

    Continued User Monitoring

    The wearable device tracks daily metrics and syncs them to the app, which updates visual reports and a color-coded calendar for monthly health trends.
    The app's model continuously adapts and learns from incoming user data, enhancing predictive accuracy over time.

  • The Team

    • Tanush Das

      Tanush Das

      Web Development
    • Maanas Saxena

      Maanas Saxena

      Data Pre-processing and Analysis
    • Aniketh Kini

      Aniketh Kini

      ML Model Deployment
    • Jugtoj Sidhu

      Jugtoj Sidhu

      Android App Development
  • Home
  • Mission
  • Works
  • Team