The problem FocusBuddy solves

Engaging Learning Experience: Say goodbye to mundane learning routines. Focus Buddy offers an exciting and interactive approach to education, making learning enjoyable for children with ADHD.
Diverse Game Library: With a vast array of games catering to various interests and skill levels, Focus Buddy ensures there's something for everyone. From math puzzles to memory challenges, each game is designed to stimulate different cognitive abilities.
Innovative Camera Tracking: Our real-time camera tracking system revolutionizes monitoring without human interaction. By respecting privacy and maintaining dignity, it provides valuable insights into a child's focus and engagement without dampening their spirits.
Comprehensive Progress Reports: Dive into detailed reports generated from gameplay. Each game is strategically linked to specific skill areas like math, logic, or memory. These reports offer a clear understanding of strengths and areas needing improvement, empowering caregivers to tailor support accordingly.
Proactive Intervention: Focus Buddy doesn't just identify areas of weakness; it prompts action. Low scores trigger alerts, signaling the need for early intervention and support. By catching challenges in their infancy, we ensure every child receives the necessary assistance to thrive.

Challenges we ran into

Integration of Camera:

Integrating camera functionality proved challenging due to compatibility issues and varying hardware specifications. We overcame this hurdle by extensively testing different camera modules and implementing robust error handling mechanisms to ensure seamless integration across devices.
Real-time Database Logging:

Establishing real-time database logging presented challenges in terms of synchronization and data consistency. We addressed this by implementing asynchronous data handling techniques and optimizing database queries for efficiency.
OpenCV Model Development (Haarcascade):

Developing Haarcascade models using OpenCV posed challenges, particularly in fine-tuning parameters and achieving desired accuracy. We addressed this by leveraging community resources, conducting thorough experimentation, and refining the model iteratively until satisfactory results were achieved.
Integration of OpenCV Model with React Frontend:

Integrating the OpenCV model with the React frontend proved challenging due to the lack of comprehensive documentation and resources. To overcome this obstacle, we adopted a trial-and-error approach, closely collaborating with backend and frontend teams to bridge the gap and ensure seamless integration.
Optimizing Loading and State Management:

Optimizing loading times and managing application state efficiently was crucial for providing a smooth user experience. We tackled this challenge by employing techniques such as lazy loading, code splitting.. Additionally, rigorous testing and performance profiling helped identify and resolve any lingering issues.