StudyBuddy

StudyBuddy detects distractions like face-touching during study, tracks focus, and gives AI summaries to help students improve habits and stay productive.

Comment

StudyBuddy Project Overview

Inspiration

We noticed that small habits like touching your face, looking away, or getting distracted can significantly break focus and even spread germs. Many students aren’t aware of these habits while studying, making it hard to improve. We wanted to create a tool that keeps users aware of distractions and helps them develop better study habits over time.

What It Does

StudyBuddy is an AI-powered focus tracker that:

  • Uses your webcam to detect when you touch your face or lose focus.
  • Tracks focus sessions and calculates a focus score using the formula:

$$ \text{Focus Score} = 100 - \left( \frac{\text{distraction time}}{\text{total time}} \times 100 \right) $$

  • Generates AI summaries after each session to give insights into performance and areas for improvement.
  • Helps students understand their habits, improve focus, and make study time more productive.

How We Built It

  • Frontend: React for a smooth, interactive user interface.
  • Backend: FastAPI to handle session data, focus calculations, and AI requests.
  • Tracking: MediaPipe for real-time face and hand detection.
  • Database: MongoDB to store session history and user progress.
  • Caching: Redis for faster retrieval of recent session data.
  • AI Insights: Gemini API generates session summaries and actionable feedback.

Challenges We Ran Into

  • Learning and implementing Google’s MediaPipe for accurate face and hand tracking.
  • Handling Git and GitHub workflows for the first hackathon experience.
  • Syncing frontend and backend efficiently through FastAPI.
  • Tuning detection parameters to minimize false positives and ensure real-time responsiveness.

Accomplishments We’re Proud Of

  • Implemented real-time face detection that accurately recognizes when a hand touches the face.
  • Built a fully functional system that tracks focus sessions, caches data with Redis, stores session history, and provides AI-generated feedback in real time.
  • Created a smooth user experience despite the challenges of integrating computer vision and backend logic.

What We Learned

  • Full-stack development from scratch using React, FastAPI, MongoDB, and Redis.
  • Handling real-time data synchronization between frontend and backend.
  • Practical experience with computer vision tools like MediaPipe and fine-tuning detection accuracy.
  • Importance of team collaboration, communication under pressure, and resolving merge conflicts effectively.

What’s Next for StudyBuddy

  • Habit streaks to encourage consistent focus sessions.
  • Posture tracking to promote healthier study habits.
  • Leaderboards to motivate users through gamification and social accountability.
  • Continuous improvement of AI insights for more personalized and actionable feedback.

Built With

Share this project:

Updates