NCT03915119

Fuzzy AI Using VR for Collision Prevention

Genetic Fuzzy Artificial Intelligence Driven Virtual Reality for Prevention of Collision-based Injury

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 45 (actual)
• Sponsor: Children's Hospital Medical Center, Cincinnati · Academic / Other
• Sex: All
• Age: 14 Years – 22 Years
• Healthy volunteers: Accepted

Summary

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.

Detailed description

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.

Conditions

• Injuries

Interventions

Type	Name	Description
OTHER	GFT AI training	navigates a cluttered environment of stationary and moving/pursuing virtual obstacles to reach a way-point as quickly and efficiently as possible. Block order and difficulty, as well as the behavior of the obstacles in each block, will be driven by the AI and statistically weighted to specifically target the perceptual-motor and neuromechanical mechanisms based on each athlete's visit 1 performance

Timeline

• Start date: 2018-01-01
• Primary completion: 2019-02-01
• Completion: 2019-09-30
• First posted: 2019-04-16
• Last updated: 2020-02-07

Locations

1 site across 1 country: United States