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通过深度学习模型,区分flexor digitorum brevis和soleus在站立姿势中的活动

Alireza Kamankesh¹, Negar Rahimi¹, Ioannis G Amiridis²

¹Department of Integrative Physiology, University of Colorado Boulder, CO, USA.

|December 14, 2024

概括

深度学习模型使用电肌图 (EMG) 数据准确区分站立姿势. 屈曲指骨肌 (flexor digitorum brevis muscle) 在不同姿势中表现出更明显的EMG活动模式,而不是肌肉.

科学领域:

生物力学和运动控制
计算神经科学是一种神经科学.
生物医学工程生物医学工程

背景情况:

电肌图 (EMG) 记录突出了柔指和肌肉肌肉在保持站立平衡中的作用.
对于这些肌肉中的EMG活动在不同的站立姿势中如何变化,人们的理解有限.

研究的目的:

开发和应用深度学习模型,以区分EMG活动在 flexor digitorum brevis和soleus肌肉之间.
分析四种不同的站立姿势的EMG信号变化.

主要方法:

利用深层卷积神经网络来分类站立姿势.
从高密度表面EMG信号中分析了时间和空间特征.
在健康的年轻人中,记录了双脚,双脚,一脚和脚尖姿势期间曲的指尖肌肉和单脚肌肉的EMG数据.

主要成果:

深度学习模型使用flexor digitorum brevis EMG数据 (84.6%) 与soleus肌肉 (79.1%) 相比,实现了更高的分类准确性.
对这两种肌肉来说,分类准确性在两种姿势之间差异很大.
Flexor digitorum brevis在双脚/双和单脚/脚尖姿势之间显示出明显的准确性差异,而soleus仅在双脚和单脚姿势之间有显著差异.

结论:

flexor digitorum brevis 肌肉在不同站立姿势的时间空间EMG活动特征上的调整比索莱斯肌肉更明显.
深度学习有效地根据肌肉特定的EMG信号区分姿势变化.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.柔软的数字短的.高密度的EMG是一种高密度的EMG.姿势姿势姿势索莱乌斯 (Soleus) 是一个阳光.

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