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☕ Caffeine Intake Recommender ☕

Your Personalized Guide to Smarter Caffeine Consumption 🚀

"Not all caffeine hits are created equal!"
Boost your productivity, while staying healthy with just the right amount of caffeine tailored to your body and your goals.


🎯 Overview

Do you feel sluggish after drinking too much coffee? Or maybe you’re crashing halfway through your day? We created the Caffeine Intake Recommender to make sure that you’re consuming caffeine in the most optimal, healthy way possible. Our app uses real-time health data, caffeine sensitivity tracking, and smart algorithms to recommend how much caffeine you should consume to meet your productivity goals, without hitting that jittery wall.

Using the app is simple: just input your goals and preferences, and based on your past data, our algorithm recommends the best caffeine intake for you to feel focused, energized, and healthy!


🌟 Core Features

🔍 Caffeine Tracking and Recommendation

  • Our app tracks your daily caffeine intake through an initial questionnaire and monitors your responses to caffeine over 5 days to understand how caffeine affects you.
  • Based on factors like your caffeine tolerance, time of day, sleep data, and heart rate, the app recommends the perfect amount of caffeine to consume for each day and goal session.

Real-Time Health Metrics

  • The app pulls data from Zepp Smart Watch, including heart rate, stress levels, and sleep patterns to understand how caffeine impacts your body. This personalized approach ensures you’re getting the right caffeine amount for you.

💻 Smart Algorithms for Personalized Advice

  • We leveraged MindsDB's machine learning models to predict your optimal caffeine dose. The algorithm continuously adapts based on your evolving patterns to give you the best advice day after day.

🖥 User-Friendly Interface

  • Whether you're a coffee lover or prefer energy drinks, our interface is designed to be intuitive and easy to navigate. Just plug in your preferences and let the app do the rest!

🛠️ How It Works

  1. Initial Setup
    During setup, you’ll fill out a quick questionnaire about your caffeine habits—what you drink, when you drink it, and how often.

  2. 5-Day Monitoring
    Over the first 5 days, the app will collect real-time data from your Zepp Smart Watch to monitor how caffeine affects your body, particularly focusing on:

    • Sleep patterns: How well do you sleep post-caffeine?
    • Stress levels and heart rate: How does caffeine impact your stress and heart metrics?
  3. Personalized Recommendations
    After the data collection, our app will start recommending caffeine amounts tailored to your unique physiology, preferences, and daily goals—whether it’s acing an exam, powering through work, or just enjoying a calm and productive day.


🔧 How We Built It

This project combines cutting-edge tech with deep research into caffeine’s effects on human performance. Here’s a glimpse of the tools and tech:

  • Intel Data: We used Intel datasets to train our algorithm and predict caffeine tolerance.
  • MindsDB: MindsDB’s pre-trained models help predict the exact caffeine dosage you need based on real-time and historical data.
  • Zepp OS: We integrated with Zepp Smart Watches to collect sleep, heart rate, and stress level data.
  • Figma: Our UI/UX was designed on Figma, focusing on simplicity and usability for the end user.
  • JavaScript: The app backend, built in JavaScript, powers the data processing and integrates with external APIs for real-time insights.

🚀 What’s Next?

The Caffeine Intake Recommender is just getting started! Here’s what we have in store:

  • Advanced Questionnaires: More detailed questions on factors like age, medications, and lifestyle for hyper-personalized recommendations.
  • Expanded Integration: Bringing in data from other fitness apps and AI assistants like Siri or Alexa for hands-free tracking.
  • Daily Logs: Let users track their caffeine intake over time and offer deeper insights into their caffeine habits.

📸 Screenshots

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💡 More Info

Curious about our process, challenges, and how we built this? Check out our full story on Devpost!

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