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深度学习用于增强假肢控制:实时运动意图解码用于同时控制人工四肢.

Jan Zbinden, Julia Molin, Max Ortiz-Catalan

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |February 29, 2024
    PubMed
    概括

    深度学习模型通过精确地从电肌图 (EMG) 信号中解码运动意图,显著改善了假肢控制. 这一进步为截肢者提供了更精确,更可靠的假肢功能.

    科学领域:

    • 康复工程 康复工程 康复工程
    • 生物医学信号处理
    • 医疗保健中的人工智能

    背景情况:

    • 先进的假肢设备需要与日常生活无整合.
    • 从电肌图 (EMG) 信号中解码运动意图对于直观的假肢控制至关重要.
    • 浅层神经网络在捕获复杂的EMG信号模式方面存在限制.

    研究的目的:

    • 为了比较深度学习架构与浅层网络的性能,用于电机意图解码.
    • 评估不同神经网络模型在实时假肢控制中的有效性.
    • 评估深度学习模型在不同用户群体中的通用性.

    主要方法:

    • 评估了四种神经网络架构:浅送,深送,时间卷积网络和卷积神经网络与挤压和激发.
    • 实时,人类在循环中的实验与有能力的参与者和一个截肢的个人进行.
    • 电肌图 (EMG) 信号被用来解码运动意图.

    主要成果:

    • 深度学习架构在解码运动意图方面明显优于浅层网络.
    • 深度网络中的表示学习有效地从EMG信号中提取了运动控制信息.
    • 使用深度神经网络的性能改善在有能力和截肢的参与者之间是一致的.

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    结论:

    • 与浅层网络相比,深度神经网络为假肢控制提供了更高的性能.
    • 使用深度学习的增强运动意图解码可以导致更可靠,更精确的假肢功能.
    • 这种方法有可能显著改善截肢患者的假肢能力和生活质量.