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基于深度学习的中风后肌电手势识别:从特征构建到网络设计

Tianzhe Bao, Zhiyuan Lu, Ping Zhou

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |March 3, 2025
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    概括

    深度学习模型显示,使用表面电肌图 (sEMG) 信号识别中风后的手势是有前途的. 频率特征和先进的神经网络显著提高了对中风康复的识别精度.

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经康复疗法 神经康复疗法
    • 机器学习 机器学习

    背景情况:

    • 机器人辅助康复可以提高中风患者的训练强度,并减少治疗师的工作量.
    • 表面电肌图 (sEMG) 是辅助技术的潜在控制来源.
    • 精确的手势识别对于有效的中风后运动恢复至关重要.

    研究的目的:

    • 研究深度学习 (DL) 对于使用sEMG信号进行中风后手势识别的潜力.
    • 评估不同的sEMG功能域,数据结构和神经网络架构.
    • 评估后处理算法的对识别准确性的影响.

    主要方法:

    • 收集了八名慢性中风患者的sEMG信号.
    • 评估了18个DL模型 (CNN,CNN-LSTM,CNN-LSTM-Attention) 使用1D和2D格式的时间,频率和波量特征.
    • 执行了学科内部和学科间的转移学习任务.
    • 分析了模型投票和贝叶斯融合后处理算法.

    主要成果:

    • 在人体内测试中,具有二维频率特征的CNN-LSTM获得了最高的准确性 (72.95%).
    • 对于跨学科转移学习,CNN-LSTM-注意与1D频率特征产生了最高的准确性 (68.38%).

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  • 频率特征在时间和波形特征上表现出显著的优势.
  • 后处理,特别是模型投票,提高了准确度高达2.03%.

    • 结论:

    • 深度学习模型,特别是CNN-LSTM和CNN-LSTM-Attention,对于基于sEMG的中风后手势识别非常有效.
    • 频域特征是最适合这种应用的.
    • 后处理算法可以进一步提高DL模型在中风康复中的性能.