AI Prediction of Kendo Helmet Insert Efficacy : Assessing Concussion Prevention Potential

Concussions pose a significant risk in sports, including kendo. This study aims to use artificial intelligence (AI) to predict the concussion prevention potential of helmet inserts with different properties. The primary research question was whether AI can accurately predict the force reduction provided by various kendo helmet inserts, when compared to actual physical testing. The insert pads were classified based on their difference between the original thickness and the thickness when compressed under load. All the inserts tested were cotton-based, commercial, off-the-shelf products. A random forest machine learning algorithm was used to create the AI model. The study found that the AI-predicted average force received by a kendo helmet insert was statistically indistinguishable from the actual measured force (p-value = 0.38), with a 95% confidence interval that closely matches the observed data. These findings suggest that AI can be a reliable tool for assessing the protective potential of kendo helmet inserts, offering a cost-effective alternative to physical testing. This study demonstrates the feasibility of using AI to predict the protective performance of a kendo helmet insert, which could pave the way for future innovation in sports safety equipment.