School of Science of physical culture and sports, Kunsan National University, Kunsan, 54150, South Korea.
College of Sports and Health, Linyi University, Linyi, 276000, China.
Sci Rep. 2024 Nov 28;14(1):29639. doi: 10.1038/s41598-024-81437-4.
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.
本研究旨在通过整合运动营养辅助和人工智能神经网络，全面探讨健身操运动员表现的影响因素。首先，对运动员的营养需求进行个性化评估和分析，收集各种数据，包括体能测试、生理监测和调查，以建立运动员个性化的营养需求模型。为了更全面地了解有氧运动员的特点和要求，将运动数据与营养数据相结合，并使用神经网络算法进行深度学习分析。此外，在人工智能技术方面，根据有氧运动的复杂性和特点，采用了 ShuffleNet V3 和 Inception V3 等优化算法。此外，引入通道注意力机制来提高模型的识别精度。最后，提出了一种基于 ShuffleNet V3 的有氧操分类识别模型。该模型通过整合运动营养、ShuffleNet V3 和注意力机制，实现了对有氧操的准确分类和识别。结果表明，该模型在准确性和 F1 分数上优于卷积神经网络（CNN）基线算法。在 MultiSports 数据集上，该模型的准确率达到 95.11%，比其他模型高出 2.66%。在自建数据集上，准确率达到 96.73%，比其他算法高出 2.56%。这表明，该模型在运动营养辅助的有氧操运动分类识别方面具有显著的准确性，为深度学习与运动科学研究的更全面交叉做出了贡献。
