GuradianAI

Our solution leverages Garmin's wearable technology to continuously monitor vital signs, ensuring immediate detection of health anomalies by integrating data analytics and machine learning algorithms.

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Inspiration

Every day, we wear devices that collect a lot of personal data, but most of this data isn't used to directly help us, especially in emergencies. Emergency Guardian changes this by turning passive health data collection into active, immediate care. It ensures that the information collected by wearable devices is used to protect users when they need help the most.

What It Does

Emergency Guardian uses Garmin smartwatches to monitor users' health data and automatically detect emergencies. Here's how it works:

  1. The smartwatch sends health data and alerts to our server.
  2. MongoDB safely stores health data for tracking.
  3. An AI model checks the data for signs of emergencies.
  4. A regression model calculates how likely an emergency is.
  5. Google Maps Geocoding API identifies the user's exact location.
  6. Gemini AI creates clear summaries of the emergency.
  7. Twilio makes automated calls to caregivers or healthcare providers.
  8. In serious cases, alerts go directly to emergency services (911).

Caregivers and family can easily check health statuses and get real-time alerts through our dashboard.

How We Built It

We built Emergency Guardian using:

  1. Wearable Integration: Created an API to collect data from Garmin smartwatches.
  2. Server Setup: Used Node.js to manage health data in real-time.
  3. AI Analysis: Trained an AI model to recognize real emergencies accurately.
  4. Location Tracking: Added Google Maps Geocoding API for precise location data.
  5. Automated Alerts: Set up Twilio to send emergency voice calls.
  6. Risk Assessment: Built a regression model to determine emergency likelihood.

Challenges We Faced

We overcame several challenges:

  1. Battery Efficiency: Improved battery life on smartwatches through smarter data handling.
  2. Reducing False Alarms: Enhanced AI accuracy to identify real emergencies only.
  3. Privacy Protection: Made sure all sensitive user data is securely stored.
  4. Quick Response: Made the system fast enough to respond immediately to emergencies.
  5. Integration: Connected different technologies like Garmin, MongoDB, AI, and Twilio smoothly.

What We Learned

We learned valuable skills including:

  • Combining wearable technology with software solutions
  • Using AI to make fast, reliable decisions
  • The importance of speed and accuracy in emergencies
  • Balancing automated systems with human judgment
  • Securely managing sensitive user data

What's Next

Our future goals include:

  • Adding support for devices like Apple Watch and Fitbit
  • Improving AI to predict emergencies earlier
  • Allowing voice interactions to cancel false alarms
  • Developing a caregiver mobile app
  • Building partnerships with emergency services for faster response

Emergency Guardian uses technology to ensure people get immediate help during emergencies, even when they can't ask for it themselves.

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