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适应性边缘:适应型号更新用于机器意图解码与知识蒸和高效的EMG传感器系统.

Mustapha Deji Dere, Giwon Ku, Ji-Hun Jo

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |October 16, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了AdaptiveEdge,这是基于电肌图 (EMG) 的手势解码的新策略. 通过将实时,设备上的更新与离线培训相结合,AdaptiveEdge显著提高了积极康复和人机交互的准确性.

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    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.
    • 信号处理 信号处理

    背景情况:

    • 基于电肌图 (EMG) 的手势解码对于积极的康复和人机交互至关重要.
    • 生产级EMG传感器有局限性,EMG解码器由于疲劳,电极转移和不同的条件而遭受性能下降.

    研究的目的:

    • 为了提出一个低成本的EMG传感器网格和先进的解码策略,AdaptiveEdge.
    • 通过自适应模型更新来解决EMG信号干扰和提高解码器性能.

    主要方法:

    • 开发了一个低成本的EMG传感器网.
    • 实现了AdaptiveEdge,一种自适应模型更新策略,将离线培训与实时设备参数更新集成在一起.
    • 进行了全面的实验,以评估解码精度和资源效率.

    主要成果:

    • 适应端实现了88.66%的准确性,比没有线下培训的方法提高了10.18% (78.48%).
    • 该策略在各种EMG干扰场景中显示出显著的精度提升.
    • 优化了设备上的应用程序的内存使用和能源消耗.

    结论:

    • 适应端为基于EMG的手势解码提供了强大的解决方案,提高了准确性和效率.
    • 拟议的方法适用于资源有限的设备应用,如神经假肢.
    • 进步促进了更有效和实用的基于EMG的设备,以改善人机交互.