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根据动态EMG分解,根据不同电阻下持续的手腕角度估计.

Xinhao Yang, Baoguo Xu, Zelin Gao

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    |June 5, 2025
    PubMed
    概括

    这项研究引入了一种新方法来解码动态手腕运动期间EMG信号中的神经驱动器,从而能够在各种阻力级别中准确地估计手腕角度,从而改善人机界面.

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 人机界面 人机界面

    背景情况:

    • 通过神经驱动器估计手腕运动对于人机界面 (HMI) 至关重要.
    • 现有的研究主要侧重于同度收缩,对动态肌电图 (EMG) 在非静止运动中的分解进行的研究有限.
    • 不同阻力对动力单元 (MU) 分解和手腕角度估计的影响还没有得到充分研究.

    研究的目的:

    • 开发和验证一种新的框架,用于在动态手腕运动期间从EMG信号解码神经驱动器.
    • 研究不同阻力水平对MU分解和手腕角度估计的影响.
    • 评估在HMI应用中使用分解的神经驱动器用于手腕角度预测的可行性.

    主要方法:

    • 使用渐进的FastICA剥离 (PFP) 算法,EMG信号被细分并分解为动力单元尖峰列车 (MUST).
    • 使用线性窗口功能跟踪电机单元,以获得完整的MUST.
    • 使用多重线性回归 (LR) 和卷积神经网络 (CNN) 估计了手腕角度 (±20°),基于神经驱动器在20%,40%和60%的最大自愿收缩 (MVC) 时.

    主要成果:

    • 拟议的框架成功地确定了所有三个阻力级别的MU,实现了平均全球脉冲噪声比 (PNR) 超过20dB.
    • 在 LR 模型中,在测试的阻力级别上显示出高的确定系数 (0.92 ± 0.06,0.91 ± 0.07,0.85 ± 0.13).

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  • 此外,CNN模型也表现出强的性能 (0.88 ± 0.10,0.88 ± 0.11,0.81 ± 0.17),表明了精确的手腕角度估计.

    • 结论:

    • 从不同阻力级别的分解的神经驱动器中估计手腕角度是可行的.
    • 开发的框架提供了一种有希望的方法,通过解码动态手腕运动来增强HMI功能.
    • 这项研究对推进复杂和响应敏捷的人机界面具有重大意义.