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相关实验视频

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Measurement of Spatial Stability in Precision Grip
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库普曼驱动的握力预测通过EMG传感.

Tomislav Bazina, Ervin Kamenar, Maria Fonoberova

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |June 3, 2025
    PubMed
    概括

    这项研究预测了使用表面电肌图 (sEMG) 信号的手握力,以便更好地进行机器人康复. 这种新方法可以准确地估计和预测握力,从而改善辅助设备的控制.

    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
    • 神经科学是一个神经科学.

    背景情况:

    • 手部功能丧失对患有中风和多发性硬化症等疾病的个体的日常生活产生重大影响.
    • 机器人康复为恢复手部功能提供了有前途的工具.
    • 表面电肌图 (sEMG) 可以个性化机器人设备的力输出,以加强康复.

    研究的目的:

    • 使用单一的sEMG传感器对,准确预测中等包裹抓地时的抓地力.
    • 从sEMG信号开发数据驱动的方法来估计握力和短期预测.
    • 为了应对手部功能康复中不断增加的传感器要求的挑战.

    主要方法:

    • 收集了来自13名受试者在两个前臂位置上的sEMG数据,用手动力计验证.
    • 应用灵活的信号处理,以实现sEMG和握力之间的高交叉相关性.
    • 利用一种新的数据驱动的库普曼式方法,并使用数据提升来预测握力.

    主要成果:

    • 在掌握力估计中获得了约5.5%的加权平均绝对百分比误差 (wMAPE).
    • 在0.5秒的握力预测中,证明了~17.9%的wMAPE.
    • 发现电极定位对错误指标没有显著影响,表明了稳定性.

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    结论:

    • 开发了一种快速准确的方法来估计和预测sEMG信号的抓地力.
    • 该算法的速度 (每0.5秒批次约30毫秒) 便于在机器人康复中实时实施.
    • 该方法通过调整设备控制以适应用户的神经肌肉信号来增强个性化康复.