The rise of digital fitness has revealed the limitations of traditional gyms that depend on manual attendance and inaccurate workout tracking. FitZone is an AI-powered smart gym system that uses computer vision and machine learning to automate operations. It automatically marks attendance through face recognition via live camera, while detecting unknown persons for enhanced security. Using OpenCV and MediaPipe, the system detects exercises like squats, push-ups, bicep curls, and lunges in real-time, counts repetitions, tracks sets, and gives form feedback without manual input. The Android mobile app provides secure login, automatic attendance records, real-time workout logging, progress analytics, exercise tutorials, AI chatbot assistance, goal tracking, and notifications. Powered by FastAPI and Firebase backend with an admin dashboard, FitZone is built using Python, OpenCV, MediaPipe, and Android Studio. In conclusion, FitZone modernizes gyms by delivering accurate, automated, and intelligent fitness monitoring and assistance.
Tools: Python,FastAPI,Firebase,Android Studio,OpenCV,MediaPipe,TensorFlow,GitHub
Department: Department of Computer Science
Poster
