CrRETA

Supported by EDIH-DO, our research project CrRETA (Crossfit Remote Enhanced Training AI) aims to assist coaches in movement analysis using state-of-the-art AI methods, ultimately enabling athletes to train more effectively and, above all, more safely. In the field of remote coaching, the precise capture and evaluation of movements pose a challenge, as direct physical interaction between coach and athlete is absent.

The application of AI in movement analysis opens up new opportunities for training optimization, particularly in terms of injury prevention and performance enhancement. CrRETA aims to elevate the quality of remote coaching to a new level by not only enabling a more objective evaluation of movements but also making the interaction between coach and athlete more intuitive and efficient.

Our project addresses this issue by leveraging innovative AI technologies such as landmark detection and object detection to analyze movement patterns in detail and identify potential errors at an early stage.

The technological foundation of CrRETA is based on insights from our previous research project HAawAI, which focused on ergonomics and time studies. The methods developed in HAawAI provide a well-established approach for the automated detection and assessment of movement patterns, which is now being specifically adapted for CrossFit training. By integrating these technologies, remote coaching can be significantly improved, allowing coaches to identify problematic movement patterns more quickly, provide more precise feedback, and suggest targeted corrections.