基于人工智能的运动员焦虑状态的分析和预测
1Physical Education College of Pingdingshan University, Pingdingshan, Henan, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种新的层次聚类算法,以精确确定田径运动员的压力来源. 开发的模型准确地对运动员的焦虑进行了分类,提高了竞技表现,并为缓解压力的策略提供了信息.
科学领域:
- 运动心理学 体育心理学
- 数据科学数据科学数据科学
- 算法开发 算法开发
背景情况:
- 准确评估运动员的焦虑对于提高竞技表现至关重要.
- 识别田径运动员的压力来源对于心理状态调节至关重要.
研究的目的:
- 提出一种新且高效的等级聚类算法,用于识别田径运动员的压力来源.
- 开发一个分析模型用于运动员焦虑状态,使用辐射基函数 (RBF) 模型.
- 与现有的贝叶斯和拉格朗日模型相比,评估拟议模型的准确性和效率.
主要方法:
- 开发了一种两阶段的等级聚类算法 (分割和聚合).
- 建立了一个RBF模型,将运动员的焦虑分为身体状况,竞争状态和认知状态.
- 该模型使用来自中国田径协会网站的500名田径运动员的数据进行训练.
主要成果:
- 提出的等级聚类算法有效地确定了运动员的压力来源.
- 与贝叶斯和拉格朗奇模型相比,基于RBF的焦虑分析模型显示出更高的准确性和效率.
- 该模型成功地将运动员的焦虑分为身体,竞争和认知状态.
结论:
- 新的层次聚类算法为识别运动员压力因素提供了一种有效的方法.
- RBF模型提供了一种精确有效的方法来分析和分类运动员焦虑.
- 这项研究为田径运动中的压力管理干预提供了有价值的决策基础.
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