IEEE Trans Neural Syst Rehabil Eng. 2017 Sep;25(9):1539-1548. doi: 10.1109/TNSRE.2016.2644264. Epub 2016 Dec 22.
Pattern recognition-based myoelectric control is greatly influenced by electrode shift, which is inevitable during prosthesis donning and doffing. This study used gray-level co-occurrence matrix (GLCM) to represent the spatial distribution among high density (HD) electrodes and improved its calculation based on the using condition of myoelectric system, proposing a new feature, iGLCM, to improve the robustness of the system. The effects of its two parameters, quantization level and input data, were first evaluated and it was found that improved discrete Fourier transform (iDFT) performed better than the other three (time-domain, autoregressive, root mean square) as the input data of iGLCM, and increasing quantization level did not significantly decrease the error rate of iGLCM when it was above 8. The performance of iGLCM with iDFT as input data and 8 as quantization level was subsequently compared with previous robust approaches (time domain autoregressive, variogram, common spatial pattern and optimal less channel configuration) and its input data, iDFT. It was showed that iGLCM achieved comparable classification accuracy without shift, and significantly decreased the sensitivity to electrode shift with 1 cm (p < 0.05). More importantly, it could reduce the perpendicular shift distance to half interelectrode distance with the electrodes worn as a band around the circumference of the forearm. Combined with the small interelectrode distance of HD electrodes, it provided a way to control the effect of perpendicular shifts fundamentally, which were the main source of performance degradation. Finally, the analysis of feature space revealed that the robustness was improved by discarding information sensitivity to shift and keeping as much as useful information. This study highlighted the importance of HD electrodes in robust myoelectric control, and the outcome would help the design of robust control system based on pattern recognition and promote its application in real-world condition.
基于模式识别的肌电控制受电极移位的影响很大，而在假肢穿戴和脱下过程中，电极移位是不可避免的。本研究使用灰度共生矩阵（GLCM）来表示高密度（HD）电极之间的空间分布，并根据肌电系统的使用情况改进了其计算方法，提出了一种新的特征 iGLCM，以提高系统的鲁棒性。首先评估了其两个参数，量化水平和输入数据的影响，结果发现改进的离散傅里叶变换（iDFT）作为 iGLCM 的输入数据比其他三个（时域、自回归、均方根）表现更好，并且当量化水平高于 8 时，增加量化水平不会显著降低 iGLCM 的错误率。随后，将具有 iDFT 作为输入数据和 8 作为量化水平的 iGLCM 的性能与以前的鲁棒方法（时域自回归、变差函数、公共空间模式和最优较少通道配置）及其输入数据 iDFT 进行了比较。结果表明，iGLCM 在无移位时达到了可比的分类精度，并且在 1cm 时显著降低了对电极移位的敏感性（p<0.05）。更重要的是，当电极佩戴在小臂周围的环形带上时，它可以将垂直移位距离减小到半电极间距。结合 HD 电极的小电极间距，它提供了一种从根本上控制垂直移位影响的方法，这是性能下降的主要原因。最后，特征空间的分析表明，通过丢弃对移位敏感的信息并保留尽可能多的有用信息，提高了鲁棒性。本研究强调了 HD 电极在鲁棒肌电控制中的重要性，研究结果将有助于基于模式识别的鲁棒控制系统的设计，并促进其在实际条件下的应用。
