Effect of Deep Learning Algorithm Incorporating Attention Module Optimization on Assisted Training for Youth Running Sports

Given the limitations of traditional methods in automatic feature extraction, the study aims to improve the accuracy of posture recognition in adolescent running training through deep learning techniques. This study first constructed a human pose motion model to capture complex pose changes during the motion process. Furthermore, an algorithm framework combining convolutional neural networks and long short-term memory networks was proposed, and optimized by integrating attention modules to enhance the model’s ability to capture key action details and improve recognition accuracy. The outcomes indicated that the recognition accuracy of the raw data processed using the convolutional neural network algorithm is 0.93, which is improved to 0.96 after preprocessing, while the accuracy of the long-short-term memory network algorithm is 0.91 on the raw data and 0.95 after preprocessing. The Inception combined with long-short-term memory network algorithm optimised by incorporating the attention module has the highest accuracy of 0.98 on the preprocessed data, while that on the 0.95 on the raw data, and 1.0 on specific actions such as running and riding. It can be found that the attention module significantly improves the recognition accuracy of the algorithm and its ability to recognise complex actions. This study not only improves the way that teenagers train to run, but it also creates new opportunities for the use of deep learning methods in the context of sports-assisted training.