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基于肌电图的手势识别 适应佩戴位置的变化

Juho Lee, Mingyoung Kam, Heejoo Yoon

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    这项研究通过使用旋转增强和域适应来改善基于电肌图 (EMG) 的手势识别,以克服电极转移造成的挑战. 新方法提高了对现实应用的准确性和适应性,减少了重新校准需求.

    科学领域:

    • 生物医学工程 生物医学工程
    • 信号处理 信号处理
    • 人与计算机的交互

    背景情况:

    • 基于电肌图 (EMG) 的手势识别对于假肢和人机接口至关重要.
    • 由于对电极放置变化的敏感性,现实世界的应用受到阻碍.
    • 由于传感器定位的变化,现有的方法在性能下降方面扎.

    研究的目的:

    • 开发一种基于EMG的强大的手势识别方法,适应电极佩戴位置的变化.
    • 提高EMG系统的通用性和适应性,以便在实践中使用.
    • 为了减少在动态环境中频繁重新校准的必要性.

    主要方法:

    • 集成旋转增强以合成生成各种电极放置.
    • 无监督对抗域适应的应用,以对齐特征分布.
    • 使用Ninapro-DB5和新收集的LGEST-DB1数据集与受控的位置变化进行验证.

    主要成果:

    • 旋转增强和域调整协同提高了识别准确性.
    • 与基线相比,LGEST-DB1的准确度增加了31.93%,Ninapro-DB5的准确度增加了13.54%.
    • 对现实世界EMG手势识别的适应性显著增强.

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    结论:

    • 拟议的方法有效地解决了EMG手势识别中电极转移灵敏度的挑战.
    • 结合旋转增强和域调整,为强大的现实世界部署提供了强大的解决方案.
    • 这种方法显著减少了重新校准需求,为更实用的基于EMG的系统铺平了道路.