โก Quick Summary
This study investigates the performance of aerobics athletes by utilizing artificial intelligence neural networks combined with sports nutrition assistance. The proposed model achieved an impressive accuracy of 96.73% on a self-built dataset, demonstrating a significant advancement in the intersection of AI and sports science.
๐ Key Details
- ๐ Dataset: MultiSports dataset and a self-built dataset
- ๐งฉ Features used: Fitness tests, physiological monitoring, and nutritional data
- โ๏ธ Technology: ShuffleNet V3, Inception V3, and channel attention mechanisms
- ๐ Performance: Accuracy of 95.11% on MultiSports dataset, 96.73% on self-built dataset
๐ Key Takeaways
- ๐ค AI integration enhances the understanding of aerobics athletes’ performance.
- ๐ Personalized nutrition models are crucial for optimizing athletic performance.
- ๐ The proposed model outperforms traditional CNN algorithms in accuracy and F1 score.
- ๐ Deep learning techniques provide a comprehensive analysis of exercise and nutrition data.
- ๐ Attention mechanisms improve model recognition accuracy significantly.
- ๐ This research contributes to the growing field of sports science and AI applications.
- ๐ Published in Sci Rep, 2024.
๐ Background
The performance of athletes, particularly in aerobics, is influenced by a multitude of factors, including nutrition and training regimens. With the rise of artificial intelligence, there is an opportunity to leverage advanced algorithms to analyze these factors comprehensively. This study aims to bridge the gap between sports nutrition and AI, providing a more tailored approach to athlete performance enhancement.
๐๏ธ Study
The research involved a detailed assessment of aerobics athletes, focusing on their nutritional needs and performance metrics. By collecting data from fitness tests, physiological monitoring, and athlete surveys, the researchers developed a personalized nutritional needs model. This model was then integrated with exercise data for deep learning analysis using neural network algorithms.
๐ Results
The results were promising, with the ShuffleNet V3-based model achieving an accuracy of 95.11% on the MultiSports dataset and 96.73% on the self-built dataset. These results indicate a significant improvement over traditional models, showcasing the effectiveness of combining exercise nutrition with advanced AI techniques.
๐ Impact and Implications
This study has the potential to revolutionize how we approach athlete training and nutrition. By integrating AI technologies with sports science, coaches and nutritionists can develop more effective training programs tailored to individual athletes. This could lead to improved performance outcomes and a deeper understanding of the physiological demands of aerobics.
๐ฎ Conclusion
The integration of artificial intelligence and sports nutrition presents a groundbreaking opportunity for enhancing athlete performance. This study highlights the potential for AI to provide personalized insights that can lead to better training and nutrition strategies. As research in this area continues to evolve, we can expect to see even more innovative applications in the world of sports science.
๐ฌ Your comments
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The factors affecting aerobics athletes’ performance using artificial intelligence neural networks with sports nutrition assistance.
Abstract
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.
Author: [‘Duan Z’, ‘Ge N’, ‘Kong Y’]
Journal: Sci Rep
Citation: Duan Z, et al. The factors affecting aerobics athletes’ performance using artificial intelligence neural networks with sports nutrition assistance. The factors affecting aerobics athletes’ performance using artificial intelligence neural networks with sports nutrition assistance. 2024; 14:29639. doi: 10.1038/s41598-024-81437-4