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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

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相关实验视频

Updated: May 10, 2026

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
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使用平板电脑设备估计肩关节旋转角度和姿势估计人工智能模型.

Shunsaku Takigami1, Atsuyuki Inui1, Yutaka Mifune1

  • 1Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, Japan.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
概括

这项研究介绍了一种人工智能驱动的方法,用于准确测量肩膀旋转角度,克服传统的度仪限制. 这种新的方法结合了姿势估计,人工智能和机器学习,用于体育和康复的精确分析.

关键词:
人工智能的人工智能是人工智能.运动范围的范围.肩膀肩膀,肩膀肩膀,这是一个很好的方法.

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相关实验视频

Last Updated: May 10, 2026

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科学领域:

  • 生物力学 生物力学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 传统的度仪在精确测量复杂的肩膀关节运动,特别是内部/外部旋转方面存在挑战.
  • 肩膀的复杂的底部和移动轴使从直立位置进行角度测量变得复杂.

研究的目的:

  • 开发和评估一种用于估计肩关节内部/外部旋转角度的新方法.
  • 为了利用姿势估计人工智能 (AI) 和机器学习来克服传统测量技术的局限性.

主要方法:

  • 利用姿势估计人工智能从10名健康志愿者的肩部运动视频中提取坐标参数.
  • 训练有素的机器学习模型,包括线性回归和Light GBM,使用这些参数和智能手机角度设备测量作为基本真相.
  • 对比了不同机器学习模型在估计肩部旋转角度方面的性能.

主要成果:

  • 轻型GBM模型实现了0.999的高相关系数和0.945.95的低平均绝对误差 (MAE).
  • 线性回归模型产生了0.971的相关系数和5.778.8的MAE.
  • 开发的方法从直接面对的视图准确地估计了内部和外部旋转角度.

结论:

  • 姿势估计人工智能和机器学习的结合为测量肩膀旋转提供了精确和可访问的方法.
  • 这种人工智能驱动的方法对于分析运动表现和身体康复中的运动运动具有重要价值.
  • 这些发现表明,在非侵入性关节角度评估技术方面,有潜在的进步.