Optimizing Athlete Training and Injury Mitigation Using Fuzzy Information-Based Skeletal Motion Analysis

In modern sports, enhancing athletic performance and minimizing the risk of injuries is crucial to optimize athlete training and injury mitigation. An athlete’s efficiency, endurance, and recovery times can be improved using targeted training programs. This study aims to develop a new strategy to study different training programs for injury mitigation based on an intuitionistic fuzzy rough set (IFRS). An IFRS is vital to reduce uncertainty during data collection and analysis. IFRS bridges two significant frameworks used to reduce uncertainty in information, i.e., the intuitionistic fuzzy set (IFS) and the rough set (RS). First, we develop new similarity measures based on IFRS to enhance accuracy. Then, we use the developed similarity measures to choose the most suitable training program for injury mitigation of athletes according to the demands of modern sports. To do so, different training programs are assessed based on several crucial factors using the proposed measures. The developed measures enhance the accuracy of data collection concerning various uncertain factors crucial in injury mitigation. An example is solved to show the application of the developed measures in injury mitigation. The main contribution of the proposed study is to enhance accuracy when human opinion is modelled.