Improved Estimation Accuracy of HD-sEMG Decomposition via an Image K-means Clustering and Removal Strategy

Over the past few decades, several methods for decomposing electromyographic (EMG) signals into motor unit action potentials (MUAPs) have been reported, but there is still much room for improvement. Here, a new method for decomposing high-density surface EMG (HD-sEMG) signals is proposed. First, the firing time is extracted based on the linear minimum mean square error (LMMSE) algorithm. Second, the waveforms corresponding to the firing time are converted into images, and the images are spliced on the electrodes of the column in which the motor unit is located. Third, the images corresponding to the multiple firing times are clustered and averaged, and the motor unit action potential (MUAP) train is obtained iteratively. Finally, the extracted MUAPs are removed from the original signals, and the cycle further decomposes the residual signals. The simulation results show that the interval pulse trains can be reconstructed with a true positive rate (TPR) of greater than 91.32 ± 4.17%. According to the experimental results, 14-20 MUs were extracted, and the ratio of common discharges was determined to be 0.81 ± 0.03% via the two-source method. The simulation and experimental results show that the proposed method is accurate and efficient with potential applications in rehabilitation and motion control systems.