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相关实验视频

Updated: May 31, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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篮球中的动作识别与惯性测量单元支持的背心

Hamza Sonalcan1, Enes Bilen1, Bahar Ateş2

  • 1Computer Engineering Department, Engineering Faculty, Aydın Adnan Menderes University, Aydın 09100, Türkiye.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括

这项研究开发了一个精确的动作识别系统,使用单个惯性测量单元 (IMU) 传感器进行篮球训练. 该系统达到96.9%的准确性,为运动员提供低成本,高性能的解决方案.

关键词:
行动的认可行动的认可篮球训练 篮球训练 篮球训练惯性测量单位 (IMU) 是指惯性测量单位.机器学习是机器学习.可以穿戴的传感器.

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科学领域:

  • 运动科学 运动科学 运动科学
  • 生物机械工程 生物机械工程
  • 机器学习应用 机器学习应用

背景情况:

  • 篮球训练依赖于精确的运动分析.
  • 目前在体育运动中的动作识别方法可能是昂贵或繁的.
  • 可穿戴式传感器技术为不引人注目的运动员监控提供了潜在的解决方案.

研究的目的:

  • 开发和评估基本篮球运动的动作识别系统.
  • 通过一种高性能,低成本的可穿戴解决方案来增强篮球训练.
  • 为了尽量减少运动员在运动数据收集期间的不适.

主要方法:

  • 在可穿戴背心中使用单个惯性测量单元 (IMU) 传感器.
  • 收集了来自21名大学篮球运动员执行各种运动的数据.
  • 在数据预处理和特征提取后应用机器学习算法 (KNN,决策树,随机森林,AdaBoost,XGBoost).

主要成果:

  • 在特定参数 (窗口大小250,75%的重叠) 中,XGBoost算法实现了最高准确率96.6%.
  • 整体系统的分类准确率为96.9%.
  • 开发的系统性能优于其他单传感器动作识别系统.

结论:

  • 这项研究为篮球动作识别提供了一个新的数据集.
  • 它比较了不同特征提取和机器学习技术的有效性.
  • 为篮球开发了一种可扩展,高效和准确的动作识别系统.