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影响有氧运动员表现的因素使用人工智能神经网络与体育营养辅助
Zhiyuan Duan1, Nan Ge2, Yuanhui Kong3
1School of Science of physical culture and sports, Kunsan National University, Kunsan, 54150, South Korea.
这项研究将体育营养和人工智能与有氧运动员的体育营养相结合. 一个新的ShuffleNet V3模型准确地分类有氧运动,增强性能分析.
科学领域:
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
- 营养科学 营养科学
背景情况:
- 有氧运动员的表现受到复杂因素的影响.
- 整合体育营养和人工智能为绩效分析提供了一种新的方法.
研究的目的:
- 探索影响有氧运动员表现的因素.
- 为运动员开发个性化的营养需求模型.
- 提出基于人工智能的模型,用于有氧运动运动的分类和识别.
主要方法:
- 使用健身测试,生理监测和调查进行个性化营养需求评估.
- 深度学习分析,使用神经网络算法整合运动和营养数据.
- 开发基于ShuffleNet V3的模型,用于运动分类的道注意力机制.
主要成果:
- 拟议的ShuffleNet V3模型在MultiSports数据集上达到95.11%的准确性,在自建数据集上达到96.73%.
- 该模型在准确性和F1分数方面超过了卷积神经网络 (CNN) 的基线.
- 体育营养和AI的整合显著改善了有氧运动运动的分类和识别.
结论:
- 开发的AI模型在与体育营养辅助的有氧运动运动分类方面表现出高准确度.
- 这项研究突出了将深度学习和体育科学结合起来,以提高运动员表现分析的潜力.
- 这些发现有助于更全面地了解AI,营养和运动表现之间的交叉关系.
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