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从顺序到同时的假肢控制:从个别的地面真实EMG模式解码同时的手指运动.

Jan Zbinden, Steven Edwards

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    研究人员开发了一种使用人工数据训练肌电假肢手掌控的方法,减少了85%的训练时间. 这种方法显示了对生物四肢的直观控制的希望,尽管有效性因复杂性而异.

    科学领域:

    • 生物医学工程 生物医学工程
    • 康复技术 康复技术 康复技术
    • 神经修复品是一种神经修复品.

    背景情况:

    • 肌电动生物肢体为截肢者提供了更好的生活质量.
    • 手术重建方面的进步使假肢手的控制变得更加直观.
    • 在假肢手中控制多个自由度 (DoF) 需要广泛的标记训练数据.

    研究的目的:

    • 通过线性结合单个运动数据,评估一种新的方法来生成标记的同步运动数据.
    • 评估训练有素的分类器对这些人工数据的有效性,以解码多DoF假肢手中的运动意图.
    • 确定这种数据增强技术对不同DoF复杂性的培训时间和性能的影响.

    主要方法:

    • 开发了一种技术,通过单个运动数据集的线性组合来创建人工标记的同时运动数据.
    • 训练有素的机器学习分类器使用人工和地面真相数据集.
    • 对3 DoF和5 DoF假肢手控制的实时指部运动解码的评估分类器性能.

    主要成果:

    • 在人工数据上训练的分类器与在地面真实数据上训练的分类器对3 DoF指动解码的表现相似.
    • 对于更复杂的5 DoF指控任务,人工数据方法的有效性下降了.
    • 拟议的方法可将获取标记数据用于分类员培训所需的时间缩短高达85%.

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    结论:

    • 线性结合个体运动数据是一种可行的策略,可以减少标记数据采集时间,用于训练肌电假肢控制,特别是较低的DoF.
    • 需要进一步的研究来优化这种方法,以实现高度复杂的多DoF假肢手掌控.
    • 这种数据增强技术显示了加速先进生物四肢的发展和个性化的巨大潜力.