With the speedy prosperity of artificial intelligence and sensor technology, the application of action recognition in football is becoming increasingly widespread. However, due to the rapid changes, complex dynamics, and diverse characteristics of football movements, traditional recognition methods face significant challenges in terms of real-time performance and accuracy. Based on this background, a football recognition model combining network wearable sensors and improved support vector machine classification algorithm is proposed. Firstly, by integrating the attitude data of accelerometers and gyroscopes, real-time dynamic features such as pitch angle, roll angle, and yaw angle are calculated, and principal component analysis is used for dimensionality reduction processing. Subsequently, the support vector machine model is optimized based on Gaussian kernel function and dynamic weighting strategy to improve classification accuracy and stability. Finally, a football action recognition model is constructed by combining wearable sensors with an improved support vector machine classification algorithm. The experiment outcomes show that the improved support vector machine algorithm achieves an action recognition accuracy of 93.8%, a recall rate of 92.5%, an F1 value of 0.92, and an inference time of 10.2 ms, which are significantly better than the comparative algorithm. In practical applications, the recognition model built has an accuracy rate of over 90% in recognizing four types of actions: standing, running, passing, and shooting, with an average recognition time as low as 9.4 ms. The research provides an efficient solution for intelligent football action recognition technology and lays the foundation for the practical application of multi-modal data fusion.
Yuan Gao (Wed,) studied this question.