深度神经网络用于在线识别来自高密度表面肌电图的运动单元活动
概括
从高密度表面电肌图 (HD-sEMG) 中识别运动单元 (MU) 活动的深度学习方法显示出有前途. 双向封闭循环单元 (Bi-GRU) 模型优于卷积神经网络 (CNN),特别是具有足够的训练数据.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 深度学习 (DL) 方法为从高密度表面电肌图 (HD-sEMG) 中识别运动单元 (MU) 活动提供了更高的稳定性和实时性能.
- 以前的研究集中在空间或时间神经网络上,独立地用于MU识别.
- 优化DL模型以实现准确和高效的MU分解仍然是一个活跃的研究领域.
研究的目的:
- 为了比较空间 (CNN) 和时间 (Bi-GRU) 神经网络的实时性能,从HD-sEMG中识别MU活动.
- 评估不同培训数据集大小对这些DL模型性能的影响.
- 为增强基于DL的MU识别技术提供见解.
主要方法:
- 利用模拟和实验HD-sEMG记录从绑架者pollicis brevis肌肉使用一个8x8电极阵列.
- 采用卷积神经网络 (CNN) 进行空间特征提取,采用双向封闭循环单元 (Bi-GRU) 进行时间特征提取.
- 通过在线HD-sEMG分解的自动渐进FastICA剥离算法识别的线下MU尖端列车使用训练的神经网络.
主要成果:
- 与CNN方法相比,Bi-GRU方法表现出更高的性能,在模拟和实验数据集上实现了更高的匹配率.
- 在使用有限的数据样本进行训练时,CNN和Bi-GRU模型都表现出性能下降.
- 该研究证实了时间信息和适当的培训数据在基于DL的MU识别中发挥的关键作用.
结论:
- 时间信息处理,特别是使用Bi-GRU,对于从HD-sEMG有效的实时MU识别至关重要.
- 训练数据的数量显著影响了DL模型对MU分解的性能.
- 这些发现为优化DL模型设计和提高HD-sEMG分解的准确性提供了宝贵的指导.
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