Invariant Surface EMG Feature Against Varying Contraction Level for Myoelectric Control Based on Muscle Coordination
Invariant Surface EMG Feature Against Varying Contraction Level for Myoelectric Control Based on Muscle Coordination
复制标题
基于肌肉协调的肌电控制针对不同收缩水平的不变表面肌电特征
DOI：
10.1109/jbhi.2014.2330356
复制
发表时间：
2015-05-01
影响因子：
7.7
通讯作者：
Zhu, Xiangyang
中科院分区：
文献类型：
--
作者：
He, Jiayuan;Zhang, Dingguo;Zhu, Xiangyang
肌肉收缩努力的变化对基于模式识别的肌电控制的性能具有实质性影响。虽然将改变纳入训练阶段可能会降低效果，但会增加训练时间，并限制临床可行性。力的调节依赖于多块肌肉的协调，这提供了对具有不同力的运动进行分类的可能性，而无需添加额外的训练样本。本研究从频域角度探讨了肌肉协调的特性，发现在不同的力水平下，同一运动的频带内肌肉激活模式向量的方向是相似的。随后提出了基于离散傅立叶变换和肌肉协调的两种新特征，在对三种不同力水平的九类运动进行分类时，分类精度比传统时域特征集提高了约11%。进一步分析发现，这两个特征减小了同一运动的不同力之间的差异p <; 0.005），并保持了不同运动之间的距离p > 0.1）。该研究还提供了一种潜在的方法，可以在无需所有力量水平训练的情况下同时对手部运动和力量进行分类。
Variations in muscle contraction effort have a substantial impact on performance of pattern recognition based myoelectric control. Though incorporating changes into training phase could decrease the effect, the training time would be increased and the clinical viability would be limited. The modulation of force relies on the coordination of multiple muscles, which provides a possibility to classify motions with different forces without adding extra training samples. This study explores the property of muscle coordination in the frequency domain and found that the orientation of muscle activation pattern vector of the frequency band is similar for the same motion with different force levels. Two novel features based on discrete Fourier transform and muscle coordination were proposed subsequently, and the classification accuracy was increased by around 11% compared to the traditional time domain feature sets when classifying nine classes of motions with three different force levels. Further analysis found that both features decreased the difference among different forces of the same motion p <; 0.005) and maintained the distance among different motions p > 0.1). This study also provided a potential way for simultaneous classification of hand motions and forces without training at all force levels.