🚀 Automating Push-Up Counting with Pose Detection 🚀

I'm excited to share a recent project where I combined my love for fitness with my technical skills in computer vision and pose estimation to create an automated push-up counter using Python! 💪

🎯 My Aim:

I wanted to create a practical tool that makes fitness routines simpler and exercise tracking more efficient. By leveraging technology, my goal was to accurately count push-ups, ensure proper form, and provide real-time feedback. This tool not only helps me track my workouts but also has the potential to assist others on their fitness journeys.

📚 What I Learned:

Working on this project taught me a lot about computer vision and human pose estimation. Here are some key lessons:

• OpenCV: I mastered using OpenCV for capturing video and processing images, which is crucial for real-time applications.
• Pose Detection: I learned how to implement pose detection using the PoseModule from cvzone, which enabled precise tracking of body movements.
• Interpolation Techniques: I applied interpolation techniques to map angles to percentage values, helping to create a visual representation of push-up progress.
• Visual Feedback: I explored using cvzone to overlay graphics and text, enhancing the user interface and overall experience.

💻 The Code:

The heart of this project is capturing video input, detecting body poses, and calculating angles at key joints to count push-ups. By tracking the angles at the elbows and shoulders, the system accurately counts push-ups and provides real-time feedback on form.

🥅 My Goal:

My goal extends beyond just counting push-ups. I aim to develop a comprehensive fitness tool that can include various exercises, offering users a versatile and reliable workout companion. By integrating real-time feedback and progress tracking, this tool can help users stay motivated and achieve their fitness goals more effectively.

🔍 Future Scope:

1. Exercise Variety: Expand the functionality to count different exercises, such as squats, lunges, or jumping jacks, by detecting and analyzing various body movements.
2. Form Correction: Integrate real-time feedback to ensure proper form and prevent injuries by highlighting incorrect postures. This could include alerts or suggestions for adjustments.
3. Performance Tracking: Add features to track and display progress over time, providing valuable insights and motivation. This could include charts, graphs, and personalized workout plans.
4. Mobile Integration: Develop a mobile app to make the push-up counter accessible on-the-go, using the phone's camera for pose detection. This would increase accessibility and convenience for users.
5. AI & ML: Use machine learning algorithms to personalize workout plans based on the user's performance and goals. By analyzing user data, the system can recommend exercises and intensity levels tailored to individual needs.

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