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    科学领域:

    • 生物医学工程 生物医学工程
    • 机器人技术 机器人技术 机器人技术
    • 神经科学是一个神经科学.

    背景情况:

    • 电肌图 (EMG) 控制系统在准确性,延迟性和稳定性方面面临着挑战.
    • 现有的系统都在与同度和非同度肌肉收缩作斗争.
    • 人类运动控制为适应性和流的机器人交互提供了洞察力.

    研究的目的:

    • 开发一种新的实时神经控制器,用于意志机器人和计算机控制.
    • 通过解决准确性,延迟和稳定性来改善EMG控制.
    • 为了在各种类型的肌肉收缩过程中实现无运动控制.

    主要方法:

    • 开发了一个EMG驱动的肌肉骨模型用于神经命令翻译.
    • 结合EMG信号处理,神经激活动态和Hill型肌肉建模.
    • 集成的肌肉激活动态与阻抗控制适应性相互作用.

    主要成果:

    • 在控制用于下肢运动的机器人执行器时实现了高参考跟踪性能.
    • 实时嵌入式计算的最新处理时间为2.9ms.
    • 在不同速度的静态和动态运动中展示了强大的控制.

    结论:

    • 这种新的控制器为基于EMG的人机接口提供了快速,准确和可适应的性能.
    • 这种方法提高了对电极变化和信号噪声的稳定性.
    • 在各种应用中为下一代神经机器接口奠定了基础.