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从可穿戴传感器网络到无标记运动捕捉,用于用于提升活动中的基于仪器的生物机械风险评估.

Irene Gennarelli1,2, Tiwana Varrecchia2, Giorgia Chini2

  • 1Department of Mathematics, Computer Science and Physics, University of Udine, Via Palladio 8, 33100 Udine, Italy.

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概括
此摘要是机器生成的。

无标记器 (ML) 运动捕获为手动物料处理提供了准确和可重复的生物机械风险评估,在评估举重任务时优于可穿戴传感器,并帮助人工智能开发以预防伤害.

关键词:
生物机械风险评估没有标记的运动捕捉.可穿戴式传感器网络

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科学领域:

  • 人体工程学和职业生物力学
  • 人与计算机的交互
  • 可穿戴技术可穿戴技术

背景情况:

  • 手动物料处理是与工作相关的腰部疾病的主要原因.
  • 准确的生物机械风险评估对于有效的预防策略至关重要.
  • 现有的可穿戴传感器网络在人体工程学评估方面存在局限性.

研究的目的:

  • 为了将无标记 (ML) 运动捕捉与可穿戴传感器网络进行比较,以评估举重任务.
  • 评估ML系统在计算生物机械风险指标 (RWL,LI) 的准确性和一致性.
  • 探索ML在自动生物机械风险分类中训练AI算法方面的潜力.

主要方法:

  • 28名工人在不同的危险条件下执行标准化的起重任务.
  • 使用可穿戴传感器网络和多摄像头ML系统捕获动力学变量.
  • 分析了数据,根据修订的NIOSH方程计算推体重限制 (RWL) 和举重指数 (LI).

主要成果:

  • ML系统与参考基准更接近,与可穿戴传感器相比变化更低,除了低风险水平 (LI=1) 外.
  • 对于大多数动力学测量,在两种系统之间观察到显著差异.
  • 基于ML的动力学数据实现了与可穿戴系统自动生物机械风险分类相匹配的准确性.

结论:

  • 无标记式运动捕捉技术显示出在职业人体工程学中准确,可重复和具有成本效益的生物机械风险评估的巨大潜力.
  • ML系统为可穿戴传感器提供了一个有希望的替代方案,用于评估苛刻的举重任务.
  • 机器学习方法可以为开发人工智能驱动的工具提供有价值的投入,用于自动化的人体工程学风险评估.