Identification of Hand and Finger Movements Using Multi Run ICA of Surface Electromyogram
Identification of Hand and Finger Movements Using Multi Run ICA of Surface Electromyogram
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DOI：
10.1007/s10916-010-9548-2
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发表时间：
2012-04-01
影响因子：
5.3
通讯作者：
Kumar, Dinesh K.
中科院分区：
文献类型：
--
作者：
Naik, Ganesh R.;Kumar, Dinesh K.
基于表面肌电图（sEMG）的假肢和计算机辅助设备的控制可以为用户提供接近自然的控制。不幸的是，当存在多个活动肌肉时，例如由于串扰导致的手指和手腕弯曲期间，没有合适的技术来对sEMG进行分类。独立分量分析（ICA）将信号分解为个体肌肉活动已被证明是有用的。然而，ICA是一种迭代技术，在初始化过程中具有固有的随机性。使用ICA分离的sEMG分类的平均改善非常小，从60%到65%。为了克服这个问题与随机性的初始化，多次运行ICA（MICA）的表面肌电信号分类系统已被提出和测试。实验结果表明，利用MICA对表面肌电信号中手指和手腕动作的识别准确率达到99%。
Surface electromyogram (sEMG) based control of prosthesis and computer assisted devices can provide the user with near natural control. Unfortunately there is no suitable technique to classify sEMG when the there are multiple active muscles such as during finger and wrist flexion due to cross-talk. Independent Component Analysis (ICA) to decompose the signal into individual muscle activity has been demonstrated to be useful. However, ICA is an iterative technique that has inherent randomness during initialization. The average improvement in classification of sEMG that was separated using ICA was very small, from 60% to 65%. To overcome this problem associated with randomness of initialization, multi-run ICA (MICA) based sEMG classification system has been proposed and tested. MICA overcame the shortcoming and the results indicate that using MICA, the accuracy of identifying the finger and wrist actions using sEMG was 99%.