一个双GRU注意力神经网络,可以实时识别来自高密度表面电肌图信号的运动单元
Chuang Lin1, Chen Chen1, Ziwei Cui1
1Information Science and Technology College, Dalian Maritime University, Dalian, China.
Frontiers in neuroscience
|March 28, 2024
概括
这项研究引入了一个双向门反复单元 (Bi-GRU) 网络,关注表面电肌图 (sEMG) 信号的实时分解,提高神经驱动器估计的计算速度.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 从表面电动图 (sEMG) 实时估计神经驱动器对于肌肉控制应用至关重要.
- 将sEMG信号精确分解为单个动力单元 (MU) 活动是一个关键的挑战.
研究的目的:
- 开发和评估一个双向门反复单元 (Bi-GRU) 网络,其中包括用于在线分解高密度sEMG信号的注意力机制.
- 与现有方法相比,在准确性和计算速度方面评估模型的性能.
主要方法:
- 一个带有注意力机制的Bi-GRU网络被提议用于sEMG信号分解.
- 该模型使用模拟的sEMG数据 (Fuglevand方法) 和实验数据进行训练,标签由梯度卷积内核补偿 (gCKC) 算法生成.
- 数据在120个采样点的窗口中以2048Hz的采样速率处理.
主要成果:
- 双GRU-Attention网络获得了高F1分数:模拟数据为0.974分,实验数据为0.876分,用于10 MU分解.
- 每个窗口的平均分解时间为28毫秒,相当于人类机电延迟限制.
- 与 gCKC 算法相比,该模型显示了显著提高的计算速度.
结论:
- Bi-GRU-Attention网络是一种可行的方法,可以从sEMG信号中实时分解动力单元活动.
- 这种方法在计算速度上比黄金标准的CGKC算法提供了实质性的改进,在准确度上有很小的权衡.
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