Fitness Monitoring using Raspberry Pi-powered Push-Up Counter (MediaPipe Pose Estimation Technology)

Project Description:
The project, titled "Empowering Advancing Fitness Monitoring using Raspberry Pi-powered Push-Up Counter with MediaPipe Pose Estimation Technology," aims to develop a push-up counter system using Raspberry Pi and MediaPipe pose estimation technology. By leveraging MediaPipe's advanced pose estimation algorithms, the system accurately counts push-ups based on the user's shoulder and elbow coordinates, providing an innovative solution for fitness monitoring and training.
Key Components:
1. Raspberry Pi Zero 2W: Acts as the central processing unit for running the push-up counting system and processing image data from the camera module.
2. Pi Camera Module (Better to choose High Quality Camera): Captures real-time video feed of the user performing push-ups for pose estimation and analysis.
3. Speaker: Provides audio feedback to the user by announcing the count of push-ups completed.
Project Features:
1. Pose Estimation: Utilizes MediaPipe pose estimation package to detect and track the user's shoulder and elbow coordinates during push-up exercises.
2. Push-Up Counting: Implements algorithms to analyze the relative positions of shoulders and elbows to accurately count completed push-ups.
3. Real-Time Feedback: Provides immediate audio feedback to the user through the speaker, announcing the count of push-ups as they are performed.
4. Fitness Monitoring: Enables users to track their push-up performance over time, facilitating progress monitoring and goal setting.
5. Raspberry Pi Integration: Optimized for Raspberry Pi Zero 2W hardware, ensuring efficient performance and seamless integration with OpenCV and MediaPipe.
Project Workflow:
1. Pose Estimation Setup: Installs and configures MediaPipe pose estimation package on Raspberry Pi, enabling accurate tracking of shoulder and elbow coordinates.
2. Image Processing: Utilizes OpenCV for digital image processing tasks, such as capturing and preprocessing video frames from the camera module.
3. Push-Up Detection: Develops algorithms to detect push-up repetitions based on the relative positions of shoulders and elbows in the video feed.
4. Audio Feedback Implementation: Integrates a speaker with Raspberry Pi to provide real-time audio feedback on the count of completed push-ups.
5. System Integration: Combines all components and functionalities into a cohesive system running on Raspberry Pi, ready for push-up monitoring and counting.
Benefits and Applications:
1. Personalized Fitness Tracking: Offers users a convenient and accessible tool for monitoring push-up performance and progress in their fitness journey.
2. Motivational Feedback: Provides immediate audio feedback to users, encouraging them to stay motivated and engaged during push-up exercises.
3. Accessibility: Enables individuals of all fitness levels to participate in push-up training, including beginners and experienced athletes.
4. Educational Value: Serves as an educational resource for learning about pose estimation technology and its applications in fitness monitoring.
5. Raspberry Pi Application: Demonstrates the versatility of Raspberry Pi for implementing innovative projects in fitness and health monitoring using advanced technologies.