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估计部撞击速度和加速从视频捕捉下降使用姿势估计算法.

Reese Michaels1, Tiago V Barreira1, Stephen N Robinovitch2

  • 1Department of Exercise Science, Syracuse University, 150 Crouse Dr, Syracuse, NY, 13244, USA.

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概括
此摘要是机器生成的。

人工智能姿势估计 (OpenPose) 准确地测量了老年人的部撞击速度,但与加速作斗争. 这种方法为分析秋季生物力学的实验室研究提供了一种不那么劳动密集的替代方案.

关键词:
人工智能的人工智能生物力学 生物力学在秋天的秋天,秋天的秋天.年龄较大的成年人.位置估计 位置估计

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科学领域:

  • 生物力学 生物力学
  • 老年学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 对老年人跌倒的视频分析是实验室研究的替代方案.
  • 在视频中手动标记身体部位是劳动密集型的.
  • 基于人工智能的姿势估计为自动化分析提供了潜力.

研究的目的:

  • 验证AI姿势估计 (OpenPose) 评估部撞击速度和加速在老年人跌倒.
  • 将AI衍生动力学与地面真相测量进行比较.
  • 调查AI区分摔倒反应的能力.

主要方法:

  • 分析了110个视频,其中有13名老年人经历横向跌倒.
  • 应用OpenPose来估计部位置和计算撞击动力学.
  • 对VICON (速度) 和MC10 (加速) 系统进行验证的OpenPose.

主要成果:

  • 对于部撞击速度,OpenPose的准确性很好 (MAE:0.17 m/s,MAPE:7.28%).
  • 对于部冲击加速,OpenPose的准确性很差 (MAPE:26.3%),低估了高冲击.
  • OpenPose可以根据跌落反应类型区分动力学差异.

结论:

  • OpenPose 是一种可行的工具,用于分析老年人跌倒时的部撞击速度.
  • 对于部撞击加速和各种落场景需要进一步验证.
  • 人工智能姿势估计可以帮助我们了解落的生物力学和受伤风险.