Machine Learning Approaches to Sports Injury Prevention and Athlete Performance Monitoring

Abstract

The advancement and use of machine learning (ML) and artificial intelligence (AI) in healthcare has drawn interest as a potent and promising tool to transform the industry. The intricacy of sports dynamics and the multifaceted nature of athletic performance provide obstacles to the promise of these tools for injury prediction, effectiveness analysis, individualized training, and therapy. Our goal was to provide an overview of the state of AI and ML applications in sports science, with a focus on injury prediction, performance improvement, and recovery. This study presents a comprehensive overview of AI and ML applications in sports science, with a particular emphasis on sports injury prediction, athlete performance enhancement, and rehabilitation. Furthermore, the challenges associated with implementing intelligent systems in sports medicine are discussed, along with potential directions for future research.  A Wiener filter was used to preprocess the data in order to reduce noise and restore the image. Top-level features are extracted from images using convolutional neural networks (CNN). By evaluating patient performance while completing recommended sports injury rehab exercises, the suggested method is used to measure physical rehabilitation. The suggested approach is contrasted with other conventional algorithms.