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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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使用加速度计和机器学习识别足球冲击模式.

Joseph M Mahoney1, Matthew B Rhudy2, Jereme Outerleys3

  • 1Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA, USA; Kinesiology, The Pennsylvania State University, Berks College, Reading, PA, USA; Mechanical Engineering, Alvernia University, Reading, PA, USA.

Journal of biomechanics
|August 19, 2024
PubMed
概括
此摘要是机器生成的。

机器学习准确地检测运行的足球运动模式,使用骨加速数据. 这项利用人工神经网络的技术可以集成到可穿戴传感器中,用于实时分析和伤害预防.

关键词:
加速度计的速度计.人工神经网络的人工神经网络足球运动模式 足球运动模式步行标识 步行标识可穿戴式传感器传感器

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科学领域:

  • 跑步的生物力学
  • 运动伤害预防 运动伤害预防
  • 机器学习在体育中的应用.

背景情况:

  • 越来越多的证据将跑步步履模式与过度使用伤害联系在一起.
  • 可穿戴式传感器为实时生物机械分析提供了潜力.
  • 需要有效的方法来从最小的传感器数据中检测脚踩模式.

研究的目的:

  • 开发和评估一个机器学习模型,用于实时的足迹模式分类.
  • 为了确定分类后脚,中脚和前脚打击的准确性,使用脚加速计.
  • 评估将这项技术集成到可穿戴设备中的可行性.

主要方法:

  • 从58名参与者中收集了部加速度计数据,他们以三种不同的脚动模式 (后脚,中脚,前脚) 跑步.
  • 使用人工神经网络分类器分析不同百分比的加速数据 (100%,75%,40%) 的立场阶段.
  • 采用数据驱动的方法,没有手动的功能选择或数据过.

主要成果:

  • 机器学习模型在分类脚动图案时达到高达89.9%的平均准确性.
  • 在中脚和前脚打击模式之间观察到最高的分类错误.
  • 该方法使用减少的数据集 (75%和40%的立场阶段) 证明了有效性.

结论:

  • 机器学习,特别是人工神经网络,可以从脚加速数据中准确地检测跑步步履的模式.
  • 这种方法适用于用于伤害风险评估的可穿戴设备的实时应用.
  • 可能需要进一步的细化,以改善中脚和前脚打击之间的歧视.