## 7 The Application of Dual Drivers of Materials and AI in Sports Events  
The collaborative innovation of materials science and artificial intelligence (AI) is profoundly transforming the field of sports. New materials provide fundamental support for the collection of sports data, while AI, through its powerful data processing and analysis capabilities, promotes the development of competitive sports towards greater efficiency, intelligence, and safety.  
In the high jump event, [PERSON] et al. [88] reported an AI-integrated training system in 2023. Researchers used gyroscope sensors to collect dynamic data of athletes during jumps, extracted key biomechanical features through the Lasso Regularization Algorithm, and finally achieved precise prediction of jump heights with the help of multi-layer perceptron neural networks. This system significantly reduces the cost of traditional high-speed camera systems, while providing a scientific basis for coaches to formulate personalized training plans.  
In the running event, the breakthrough of smart textile materials has brought revolutionary progress to sports monitoring. In 2022, [PERSON] et al. [89] achieved real-time monitoring of key indicators such as heart rate, muscle activity, and body temperature by integrating flexible sensors into sportswear through printing, coating or weaving. Combined with the AI gait analysis technology based on the inertial measurement unit, this system can accurately evaluate biomechanical parameters such as foot inversion and ground contact time within the speed range of 8 to 12 km per hour, thereby providing data support for running posture optimization and sports injury prevention. However, during high-speed running at speeds above 14 km per hour, the system accuracy has declined, indicating the direction for future optimization.  
In wheelchair competitive sports, the introduction of AI technology also shows great potential. [PERSON] et al. [81] have developed an extended Madgwic filtering algorithm that can accurately estimate the trunk tilt of athletes and is applicable to athletes of different skill levels and wheelchair types. Combined with the kinematic data of wheelchairs, this system can calculate key parameters such as the center of mass displacement, power output and energy consumption of athletes, providing a quantitative basis for the optimization of training programs, significantly improving training efficiency and reducing the risk of sports injuries.  
In the field of rugby, in 2021, [PERSON] et al. [90] conducted a study aimed at identifying head and body impact events during matches. They developed a machine learning-based classifier and used customized instrumentalized mouth guards in combination with video verification to collect data, covering 21,348 data records from a total of 64 elite athletes in 119 matches from the Australian Football League (AFL) and Women's Football League (AFLW). The research adopted techniques such as low-pass filtering and cross-validation to process the data, and used F1 score, true positive (TP) and true negative (TN) as evaluation indicators. The classifier shows an accuracy of more than 95% in most data subsets. The study also adopted the SHAP (SHapley Additive exPlanations) method to explain the feature weights of the model, providing theoretical support for the design of sports equipment and injury prevention.  
In conclusion, the deep integration of materials science and AI is reshaping sports training and competition models in unprecedented ways. From data collection and motion analysis, to injury prevention and equipment optimization, this dual-driven mechanism is driving sports technology towards a smarter new era.