title: Combining Improved Gray-Level Co-Occurrence Matrix With High Density Grid for Myoelectric Control Robustness to Electrode Shift.
abstract: 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.
authors.forename: Jiayuan
authors.initials: J
authors.lastname: He
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000328:Adult; D016477:Artifacts; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D006801:Humans; D008297:Male; D046228:Microarray Analysis / Q000295:instrumentation / Q000379:methods; D008839:Microelectrodes; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity