Project Proposal Feedback

[pooyanjamshidi]

Feedback on Swimming Technique Analysis Proposal:

It is very disappointing that you did not submit your proposal using the proposal template and did not follow the instructions! but anyway, I am giving this feedback based on your slides.

This project addresses a very interesting and highly relevant problem within the world of competitive swimming. The combination of neural networks and real-time 3D modeling to provide feedback on swimming techniques has significant potential to revolutionize how swimmers train and improve their form. Below are my thoughts and suggestions to help refine and develop the proposal further:

Strengths:

• Well-Defined Problem: The problem you’re tackling is very clear: swimmers struggle to identify subtle flaws in their techniques, and coaches may not always be able to catch them through traditional observation methods. You’re focusing on a data-driven solution, which aligns well with modern trends in sports technology.
• Real-World Application: This solution could provide swimmers and coaches with actionable insights into technique refinement, potentially preventing injury and improving performance. The practical benefit to athletes and trainers makes this project very appealing.
• Focus on Real-Time Feedback: The idea of real-time feedback is exciting. If your tool can effectively capture swimmer movements and provide instant analysis, it could be a game-changer in training environments.
• Innovative Use of Neural Networks: Utilizing pose estimation and 3D pose reconstruction models is a smart choice, as these are well-suited for understanding complex movements like swimming. Your proposed solution is both technologically advanced and applicable to real-world challenges in sports.

Suggestions:

1. Data Collection Challenges:

   • Data collection in the swimming environment is indeed a key challenge. Since water introduces unique visual distortions and challenges (e.g., bubbles, reflections, water turbulence), consider elaborating on how you plan to capture high-quality data. Are you planning to use underwater cameras, above-water views, or a combination of both? Also, consider the hardware requirements for capturing the necessary video footage in real-time.
   • Additionally, will you require a large labeled dataset of swimming poses and techniques? If so, how will you go about obtaining this data, and what tools or techniques will you use to label and prepare it?

2. Pose Estimation and Accuracy:

   • The pose estimation and 3D pose reconstruction models you mentioned are great choices, but achieving high accuracy in such a complex environment (underwater) may be tricky. It might be beneficial to explore methods for improving model accuracy under these challenging conditions. For example, you could look into multi-view camera setups to get more comprehensive data or augmented data training (e.g., using synthetic or simulated data to supplement real footage).
   • Another consideration is how you’ll handle occlusions (e.g., arms blocking the view of the torso) or underwater distortions. These could pose challenges for the model’s accuracy.

3. Real-Time Processing Considerations:

   • Real-time processing will require optimizing both the software and the hardware. You may want to clarify your strategy for achieving real-time performance. Will you use GPU acceleration, edge computing devices, or cloud-based processing? Balancing the computational load while maintaining accuracy and minimizing latency is critical here.
   • Consider the trade-off between accuracy and speed in real-time applications. How do you plan to prioritize these factors?

4. Quantitative and Qualitative Evaluation:

   • For quantitative evaluation, measuring swimmer performance improvements through metrics like stroke efficiency, speed, and consistency is excellent. It would also help to define specific benchmarks or improvements you're aiming to achieve with your tool. How will you measure stroke efficiency? Are there established benchmarks or standards in swimming to compare against?
   • For qualitative evaluation, you mentioned visual comparisons of techniques. Consider implementing user feedback (from coaches and swimmers) on the tool’s effectiveness. This could add more depth to the evaluation process.

5. Integration with Existing Workflows:

   • You mentioned that integrating the tool into existing coaching workflows could be a challenge. It would be helpful to expand on how you plan to address this. Will the tool be used during training sessions, or could it also be used post-session for analysis? Think about how you can make the tool as seamless as possible for both coaches and swimmers.
   • Consider adding a section that outlines the user interface (UI) and how the data will be presented to swimmers and coaches. Will it be a web-based platform, a mobile app, or integrated into existing systems?

6. Potential Expansion:

   • Once the 3D modeling system is built, you could consider expanding the project by incorporating AI-powered recommendations. For instance, the tool could not only visualize incorrect techniques but also suggest corrections or alternative movements based on the swimmer’s unique anatomy and goals.
   • You could also explore the possibility of incorporating biomechanical models to ensure that the movements recommended by the tool align with optimal biomechanics for injury prevention.

Additional Considerations:

• Model Training Strategy: Will the model be trained on pre-existing datasets of human movement, or will it require a swimming-specific dataset? Given the unique challenges of underwater motion capture, you may need to create or acquire a specialized dataset.

• Privacy and Data Security: If the system captures and stores video data of athletes, ensure that privacy and security concerns are addressed. How will you protect sensitive data? Consider mentioning how the data will be stored, anonymized, or processed securely.

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Conclusion:

This project has great potential to make a meaningful impact in the world of competitive swimming by providing swimmers and coaches with real-time, data-driven insights into technique. The combination of advanced neural networks and real-time feedback is innovative and could revolutionize the way athletes train. As you move forward, focusing on the challenges of data collection, model accuracy, and real-time processing will be key to ensuring the tool’s success. This is an exciting and ambitious project—looking forward to seeing how it evolves!