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人工智能用于客观评估杂技运动:应用机器学习来识别啦啦队运动中的动元素
Sophia Wesely1, Ella Hofer2, Robin Curth1,3
1Institute of Physical and Theoretical Chemistry, Faculty of Chemistry, Leipzig University, 04103 Leipzig, Germany.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
人工智能 (AI) 现在可以使用单个传感器客观地评估啦啦队队长的翻滚元素. 机器学习模型准确地识别机动,为演技运动中的教练和绩效评估提供了有价值的数据.
科学领域:
- 体育科学 运动科学 运动科学
- 生物力学 生物力学
- 人工智能的人工智能
背景情况:
- 啦啦队已经发展成为一种竞技运动,需要客观的绩效评估.
- 目前的啦啦队队员倒的评估方法依赖于主观判断,这给准确性带来了挑战.
- 人工智能 (AI) 在杂技运动分析中的应用有限,但显示出显著的潜力.
研究的目的:
- 调查使用AI与惯性测量单位 (IMU) 数据的可行性,以客观地评估啦啦队的翻转.
- 开发和评估机器学习 (ML) 模型,用于识别和评估翻转的元素.
主要方法:
- 来自第一师大学啦啦队的16名参与者在背骨部戴着单个IMU.
- 在练习期间,在4周的时间内记录了1102个滚动元素.
- 用各种ML算法进行分类和执行评估,分析了三轴加速度和旋转速度.
主要成果:
- 机器学习模型在识别不同的啦啦队的翻滚元素方面表现出了很高的准确性.
- 尽管在收集的数据中存在个体间的变化和噪音,但人工智能方法证明有效.
- 成功生成了用于滚动执行的客观指标.
结论:
- 使用IMU数据进行人工智能驱动的评估是可行的,以客观地评估啦啦队的翻滚元素.
- 这项技术为啦啦队和类似运动中的传统主观评判方法提供了有价值的补充.
- 该研究强调了人工智能的潜力,以增强演技运动中的绩效分析和教练.
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