Injuries Clinical Trial
Official title:
Genetic Fuzzy Artificial Intelligence Driven Virtual Reality for Prevention of Collision-based Injury
The purpose of this study is to develop and test a VR training system that integrates GFT AI with virtual obstacle scenarios that, when compared to a sham-VR training system, is hypothesized to increase neuromechanical and perceptual-motor fitness, decrease collision frequency and impact forces for soccer athletes, during a single training session and also when assessed at approximately 1 week and 1 month following training.
Player collisions cause over 70% of concussion injuries in contact sports, in addition to 50% of lower extremity injuries and 40% of catastrophic knee ligament injuries. The majority of these collisions are unanticipated, and associated with reduced neuromechanical and perceptual-motor fitness underlying an athlete's adaptability to on-field conditions. Thus, training collision anticipation necessitates a method that taps into neuromechanical and perceptual-motor fitness. Virtual reality (VR) is a tool that can target these mechanisms, while providing a safe, well-controlled environment for assessment and training. The current proposal innovates on VR with the integration of genetic fuzzy tree (GFT) artificial intelligence (AI) to drive scenario configuration designed to target modifiable mechanisms and tailored to the individual athlete's performance capabilities, for the optimization of behavior modification and skill transfer. The current study will examine test a GFT AI-driven VR collision anticipation training compared to a sham-VR training system in healthy soccer athletes. ;
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