Bits, Beats, and Better Health: My Hackathon Project Using Terra API for Heart Attack Prevention

Have you ever had that moment when a personal challenge sparks an idea that could change lives? That's exactly what happened to me during the recent MedHACKS Hackathon. Let me take you on a journey that started with a family health scare and led to an innovative project that could potentially save lives.

It was a typical evening when I decided to participate in MedHACKS, an online hackathon focused on medical innovations. As I sat brainstorming ideas, my thoughts kept drifting to my mother. She's been battling high cholesterol, putting her at risk for heart disease. That's when it hit me – what if we could detect early signs of a heart attack using data from the wearable devices so many of us use every day?

With this seed of an idea, I dove into research. The statistics were staggering: millions of Indians die each year from heart attacks, many of which could potentially be prevented with early detection. I knew I was onto something important, but I also realized I needed powerful tools to make it happen.

I made the proof of work with the help of Terra API and my development skills and now lets deep dive into it.

How I got to Terra API ?

So I was sure that I had to work on this problem statement and I knew I needed to work with wearable data, but I was a little confused about how to integrate APIs from different wearables at the same time. On the same day, I was randomly scrolling on the X platform when I stumbled upon a tweet about Terra API. Intrigued, I searched for more information and quickly realized this could be exactly what I needed for my hackathon project.

However, I soon discovered that Terra API required a subscription, which presented a challenge for me as a student working on a hackathon project. Not wanting to give up on such a promising tool, I decided to take a chance. I reached out directly to the founder of Terra, humbly asking if there was any possibility of getting free access to a Terra account for the duration of the hackathon.

To my surprise and delight, the founder responded positively to my request. He appreciated my initiative and the potential impact of my project. In a generous gesture that truly embodies the supportive spirit of the tech community, he granted me free access to Terra API for the entire hackathon period.

How I Approached the Problem?

When I first tackled this challenge, I envisioned creating a sophisticated ML model to predict heart attack risks using wearable data. However, the lack of a comprehensive dataset forced me to pivot. This constraint led me to a more creative and practical solution. Here's how I approached the problem:

1. Data Visualization Dashboard: I created a user-friendly dashboard using React. This dashboard serves as the central hub, displaying crucial health metrics like heart rate, sleep duration, daily steps, and weight. The goal was to present complex health data in an easily digestible format.

2. Comparative Scoring System: Instead of predictive modeling, I developed a comparative scoring system. This system benchmarks the user's wearable data against healthy standards across various metrics (heart rate, sleep patterns, stress levels, ECG readings, etc.).

3. Holistic Health Score: I implemented an algorithm that normalizes and combines these individual scores into an overall health score on a 10-point scale. This provides users with a quick snapshot of their general health status.

4. Alert Mechanism: A key feature is the alert system. If a user's overall score drops below 4, the system triggers an email alert. This proactive approach ensures users are promptly notified of potential health risks.

5. Detailed Health Breakdown: Beyond the overall score, I created individual scorecards for specific health aspects like Heart Score, Sleep Score, Oxygen Score, HRV (Heart Rate Variability) Score, and Stress Score. Each is visually represented with circular progress bars for easy interpretation.

6. AI-Powered Health Insights: To add more value, I integrated generative AI to provide personalized feedback based on the user's health scores. This AI analyzes the scores and generates tailored health advice and recommendations.

7. Actionable Recommendations: The system doesn't just provide scores; it offers practical, actionable recommendations to improve health. These suggestions are based on the user's specific health metrics and general best practices for heart health.

By focusing on data visualization, comparative analysis, and AI-driven insights, I created a tool that not only monitors heart health but also empowers users with actionable information. While it may not predict heart attacks with machine learning precision, it serves as an effective early warning system and health management tool.

This approach demonstrates that sometimes, working within constraints can lead to more innovative and practical solutions. By leveraging available data and focusing on user experience and actionable insights, we can create impactful health monitoring tools even without extensive datasets for machine learning models.

Conclusion

This hackathon journey transformed a challenge into an opportunity, resulting in a health monitoring dashboard that showcases the power of adaptability and user-centric design. Terra API proved invaluable, streamlining wearable data processing and allowing focus on creating user value.

The support from Terra's developers was exceptional - their responsiveness and willingness to help truly elevate the development experience. For those intrigued by this project, check out my GitHub repo. Your 🌟 and feedback are greatly appreciated!

To fellow developers: explore Terra API and its excellent documentation. It's a game-changer for health tech projects.

Remember, every coding project has the potential to make a difference. Stay curious, adapt, and keep learning.

Happy coding, and here's to tech for better health! ❤️❤️🔥🔥👨‍💻👨‍💻👨‍💻