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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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基于新型时空图形卷积网络的跨用户电肌图形识别.

Mengjuan Xu, Xiang Chen, Yuwen Ruan

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |December 13, 2023
    PubMed
    概括
    此摘要是机器生成的。

    图形神经网络 (GNN) 增强了电肌学 (EMG) 模式识别,用于肌电控制. 拟议的CNN-MSTGCN模型提高了手势识别率,减少了用户培训负担和个人差异.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 肌电控制技术的开发依赖于精确的电肌图 (EMG) 模式识别.
    • 高密度表面EMG (HD-sEMG) 信号提供了丰富的时空信息,对于先进的模式识别至关重要.
    • 现有的方法经常因个人差异和用户培训负担而扎.

    研究的目的:

    • 使用图形神经网络 (GNN) 开发一个强大的电肌图 (EMG) 模式识别解决方案.
    • 将一个卷积神经网络 (CNN) 的特征提取模块与一个多视图时空图卷积网络 (MSTGCN) 集成.
    • 评估拟议的CNN-MSTGCN模型在用户独立和转移学习场景中的表现.

    主要方法:

    • 一个新的CNN-MSTGCN模型是通过将CNN特征提取模块集成到MSTGCN分类器中来设计的.
    • 实验使用11名受试者17种手势的HD-sEMG数据进行.
    • 进行了废除研究,用户独立识别和基于转移学习的跨用户识别实验.

    主要成果:

    • 与ResNet50和LSTM相比,CNN-MSTGCN模型在EMG模式识别方面表现出显著的改进.
    • 在用户独立的测试中,CNN-MSTGCN实现了68%的识别率 (相对于ResNet50:47.5%,LSTM:57.1%).
    • 转移学习实验 (TL-CMSTGCN) 的识别率为92.3%,超过TL-ResNet50 (84.6%) 和TL-LSTM (85.3%).

    结论:

    • 拟议的CNN-MSTGCN模型有效地提高了EMG模式识别的准确性.
    • 在减轻EMG信号中个体差异的影响方面,GNN显示出前景.
    • 开发的模型为强大的肌电控制和减少用户培训提供了可行的解决方案.
    • 该研究强调了GNN在推进肌电控制技术方面的潜力.