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评估可穿戴传感器技术,用于预测肩膀耐力.

Patricia O'Sullivan, Matteo Menolotto, Brendan O'Flynn

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    可穿戴式传感器可以预测肩部任务的耐力时间,有助于预防与工作有关的伤害. 惯性测量单元为实时员工疲劳监测提供了最准确的预测.

    科学领域:

    • 生物力学 生物力学
    • 人体工程学就是人体工程学.
    • 可穿戴技术可穿戴技术

    背景情况:

    • 与工作相关的肌肉骨疾病是职业健康的一个重大问题.
    • 准确预测身体任务耐力对于预防疲劳和受伤至关重要.
    • 现有的生物机械模型往往缺乏实时,可用于动态任务的可穿戴集成.

    研究的目的:

    • 调查不同可穿戴传感器对预测动态肩部任务的耐力时间 (ET) 的影响.
    • 评估使用基于扭矩的生物力学耐力模型进行实时工人监控的完全可穿戴系统的可行性.
    • 评估一种用于在耐力模型中产生最大扭矩输入的新方法.

    主要方法:

    • 三种预测方法的比较:非可穿戴 (运动捕捉) 和两种可穿戴 (惯性测量单位,压力内).
    • 将传感器数据与基于扭矩的生物力学耐力模型集成.
    • 使用绝对平均误差进行耐力时间估计的预测准确性的分析.

    主要成果:

    • 可穿戴式传感器的集成显著影响了ET预测.
    • 惯性测量单位 (IMU) 提供了最精确的ET预测 (24.8%的绝对平均误差).
    • 运动捕捉 (30.2%的误差) 和压力内 (29.8%的误差) 显示出更高的误差,内经常低估了ET.

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    结论:

    • 可穿戴传感器,特别是IMU,显示出在职业环境中实时疲劳预测的巨大潜力.
    • 将可穿戴技术与生物机械耐力模型相结合,可以增强与工作相关的肌肉骨疾病的预防.
    • 需要进一步的研究来优化实际工作环境中的实时预测准确性.