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Updated: Sep 11, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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从上肢轨迹自动估计手部活动水平:一个概率回归框架.

Ting-Hung Lin1, Yu Hen Hu1, Robert Radwin2

  • 1Department of Electrical & Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA.

Ergonomics
|August 14, 2025
PubMed
概括

本研究引入了一种新的基于视频的方法,用于自动测量手动活动水平 (HAL),以评估重复性工作伤害风险. 该框架提供客观可靠的HAL得分与信心指标.

科学领域:

  • 人体工程学和职业健康学
  • 计算机视觉和机器学习
  • 生物机械工程 生物机械工程

背景情况:

  • 精确的手动活动水平 (HAL) 测量对于评估重复性任务中的肌肉骨损伤风险至关重要.
  • 目前的手动HAL评估是主观的,无法进行持续监测.
  • 需要客观和自动化的方法来进行可靠的人体工程学风险评估.

研究的目的:

  • 开发和验证一个概率回归框架,用于使用视频数据自动估计HAL得分.
  • 在人体工程学风险评估中为量化不确定性提供与HAL预测一起的信任度.
  • 为了使客观,可靠和可扩展的监测,在职业环境中,手动活动.

主要方法:

  • 使用基于视频的上肢姿势轨迹作为输入特征.
  • 开发了一个概率回归框架,用于HAL得分估计.
  • 嵌入的信心措施来量化预测不确定性.

主要成果:

  • 实现了强大的域内性能,根平均平方误差 (RMSE) = 0.24和平均绝对误差 (MAE) = 0.17.
  • 证明了强大的跨领域概括性,RMSE = 0.74和MAE = 0.54.
  • 该框架提供了准确和可转移的HAL预测.
关键词:
手动活动水平 手动活动水平计算机视觉 计算机视觉概率回归是一种概率回归.工作场所安全工作场所安全

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结论:

  • 拟议的基于视频的概率回归框架使客观可靠的手动活动水平 (HAL) 自动估计成为可能.
  • 通过信心测量量量化的不确定性提高了人体工程学风险评估.
  • 该方法显示了大规模和持续监测手工密集型工作的巨大潜力,提高了职业安全.