Data Collection Strategies for Preparing ML Model for Prediction of Injuries in Gymnastics

Gymnastics is a sport that is associated with a high risk of injury. Injury prevention is crucial in gymnastics to ensure the safety and well-being of athletes. Machine learning (ML) models can be used to predict the likelihood of injuries in gymnastics, but the accuracy of these models depends on the quality and quantity of the data used to train them. In this paper, we describe the data collection strategies used to gather injury data for the development of an ML model for predicting injuries in gymnastics. We discuss the strengths and limitations of these strategies and suggest areas for future research. Our findings highlight the importance of collecting comprehensive and standardized injury data to improve injury prediction models and ultimately, injury prevention in gymnastics.