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通过多机器学习决策对武术运动的有效对齐骨表示的可解释的质量评估
Yiqun Pang1,2, Kaiqi Zhang3, Fengmei Li4
1School of Physical Education, Southwest Petroleum University, Chengdu, 610500, China.
Scientific reports
|January 2, 2025
概括
本研究介绍了一种用于武术的智能机器学习评分方法,使用特征对齐和适应加权来获得客观反. 该系统实现了专家级准确性,促进了武术教育.
科学领域:
- 体育科学与技术 体育科学与技术
- 体育教育的体育教育
- 体育运动中的人工智能
背景情况:
- 武术中的客观评分和反对于体育教育至关重要.
- 当前的方法可能会受到运动员运动速度,节奏和持续时间的变化的影响.
- 需要技术解决方案来提高武术评估的准确性和客观性.
研究的目的:
- 通过机器学习为武术运动提出一种智能得分方法.
- 开发一个提供准确评分和客观反的系统.
- 为了利用可解释的人工智能为武术教学和学习反.
主要方法:
- 使用特征对齐来规范运动变化的特征提取.
- 一个适应加权的多模型决策策略,以提高客观性和准确性.
- 可解释的人工智能用于反生成的应用.
主要成果:
- 综合模型在多个数据集 (XSQ,UMONS-Taichi,PBB) 中表现出优异的性能,与其他功能调整后算法相比.
- 性能指标 (MAE,RMSE,sMAPE,R2,皮尔森相关性,ICC) 表示高精度和可靠性.
- 该系统的性能与人类专家的性能非常接近.
结论:
- 拟议的机器学习方法实现了与人类专家可比的运动评估性能.
- 这项研究为各种武术风格的自动评分提供了技术框架.
- 这些发现支持通过技术普及和发展武术教育.
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