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深度学习用于使用残留学习的电动图下肢运动信号分类.

Jiahao Sun, Yifan Wang, Jun Hou

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |May 21, 2024
    PubMed
    概括

    本研究介绍了JJ数据集,这是一个大型的,开源的亚洲人的下肢电肌图 (EMG) 信号集合,可用于人类运动识别的高级深度学习.

    科学领域:

    • 生物力学 生物力学
    • 神经科学是一个神经科学.
    • 康复工程 康复工程 康复工程

    背景情况:

    • 电肌图 (EMG) 信号越来越多地用于假肢和外骨的控制,主要是在上肢.
    • 下肢EMG研究,特别是使用多样化的种族数据和深度学习应用,仍然不发达.
    • 现有的数据集缺乏标准化和全面的肌肉覆盖,用于下肢运动意图.

    研究的目的:

    • 解决下肢EMG数据集的稀缺问题,特别是针对亚洲人群.
    • 介绍JJ数据集,一个大规模的,开源资源下肢EMG分析.
    • 研究深度学习,特别是ResNet-18的有效性,用于从下肢EMG信号中识别人类运动意图.

    主要方法:

    • JJ数据集的开发:来自15个个人的约13,350个干净的EMG段,跨越10个步行阶段,覆盖了九个主要的腿部肌肉.
    • 信号处理:利用处理的时间域EMG信号作为深度学习模型的输入.
    • 分类:采用调整后的ResNet-18架构用于人类步行阶段识别.

    主要成果:

    • 该数据集是第一个全面捕捉了参与人类步行的九个主要肌肉的数据集.
    • 实验探讨了预处理方法,信号域 (时间与频率) 和跨主体识别精度.

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  • 调整后的ResNet-18在人类步行阶段中实现了高平均分类准确率95.34%.

    • 结论:

    • 联合日报数据集为推进下肢EMG研究提供了有价值的开源资源.
    • 像ResNet-18这样的深度学习模型显示出强大的下肢人类运动意图识别的巨大潜力.
    • 这项工作强调了使用大腿和小腿肌肉EMG用于截肢应用的可行性.