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相关概念视频

Fatigue01:21

Fatigue

185
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
185

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相关实验视频

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从使用深度神经网络和由域知识告知的工程特征的面部视频进行疲劳评估.

Luke Kenworthy, Patrick Moore, Hrishikesh M Rao

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    本研究介绍了一种快速,客观的面部视频工具来检测疲劳,其表现优于主观的自我评估. 随机森林模型显示了准确识别高压工作中的疲劳水平的前景.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 认知科学 认知科学
    • 机器学习 机器学习

    背景情况:

    • 疲劳严重损害了认知和运动功能,增加了航空和紧急服务等关键职业的意外风险.
    • 目前的疲劳评估依赖于主观的自我报告,这是不可靠的,容易出错.
    • 迫切需要客观,快速和准确的方法来检测危险的疲劳水平.

    研究的目的:

    • 开发和评估一种使用面部视频分析快速检测疲劳的定量工具.
    • 为了比较长短期记忆 (LSTM) 深度神经网络与随机森林 (RF) 分类器的性能,以评估疲劳.
    • 建立一种可扩展和可访问的方法,用于在苛刻的职业中监测疲劳.

    主要方法:

    • 使用不到两分钟的通过iPad捕捉的面部视频开发了一种定量疲劳与警觉性评估工具.
    • 使用了由域名知识为基础的工程特征.
    • 将长期短期记忆 (LSTM) 深度神经网络和随机森林 (RF) 分类器之间的分类性能进行了比较.

    主要成果:

    • 与LSTM深度神经网络相比,随机森林 (RF) 分类器表现出更高的性能.
    • 射频分类器在接收器操作特征曲线下的平均面积为0.72 ± 0.16 (11倍CV) 和0.8 ± 0.12 (个性化11倍CV).
    • 射频的等错率分别为0.34和0.26,表明了强大的分类准确性.

    结论:

    • 开发的面部视频分析工具为疲劳检测提供了一个有希望的,快速和客观的方法.
    • 随机森林模型在本初步研究中提供了可解释性,并且优于LSTM.
    • 计划进一步收集数据,以提高跨不同人群的概括性.