He Jiayuan, Zhang Dingguo, Sheng Xinjun, Li Shunchong, Zhu Xiangyang
IEEE J Biomed Health Inform. 2015 May;19(3):874-82. doi: 10.1109/JBHI.2014.2330356. Epub 2014 Jun 30.
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 ) 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.
肌肉收缩力的变化对基于模式识别的肌电控制性能有重大影响。虽然在训练阶段纳入变化可以减少这种影响，但训练时间会增加，临床可行性也会受到限制。力的调制依赖于多块肌肉的协调，这为在不增加额外训练样本的情况下对不同力的运动进行分类提供了一种可能性。本研究探索了频域中肌肉协调的特性，发现对于不同力水平的相同运动，频带的肌肉激活模式向量的方向相似。随后提出了基于离散傅里叶变换和肌肉协调的两个新特征，在对具有三种不同力水平的九类运动进行分类时，与传统时域特征集相比，分类准确率提高了约11%。进一步分析发现，这两个特征都减小了相同运动不同力之间的差异，并且保持了不同运动之间的距离（p>0.1）。本研究还提供了一种潜在的方法，可在无需在所有力水平上进行训练的情况下同时对手部运动和力进行分类。
