The factors affecting aerobics athletes’ performance using artificial intelligence neural networks with sports nutrition assistance

This work aims to comprehensively explore the influencing factors of aerobics athletes' performance by integrating sports nutrition assistance and artificial intelligence neural networks. First, a personalized assessment and analysis of athletes' nutritional needs are conducted, collecting various data including fitness tests, physiological monitoring, and surveys to establish a personalized nutritional needs model for athletes. In order to gain a more comprehensive understanding of the characteristics and requirements of aerobic athletes, exercise data are integrated with nutritional data, and deep learning analysis is performed using neural network algorithms. Moreover, in terms of artificial intelligence technology, optimization algorithms such as ShuffleNet V3 and Inception V3 are employed based on the complexity and characteristics of aerobic exercise. Besides, a channel attention mechanism is introduced to enhance the model's recognition accuracy. Lastly, a ShuffleNet V3-based aerobic exercise classification and recognition model is proposed. It achieves accurate classification and recognition of aerobic exercise by integrating exercise nutrition, ShuffleNet V3, and attention mechanisms. The results reveal that this model outperforms the Convolutional Neural Network (CNN) baseline algorithm on accuracy and F1 score. On the MultiSports dataset, the proposed model achieves an accuracy of 95.11%, surpassing other models by 2.66%. On the self-built dataset, the accuracy reaches 96.73%, outperforming other algorithms by 2.56%. This indicates that the proposed model demonstrates significant accuracy in aerobics movement classification recognition with sports nutrition assistance, contributing to a more comprehensive intersection of deep learning and sports science research.