This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficiently identify motor units (MUs) without preprocessing. The proposed U-Net based network was trained by the HD-sEMG signals with innervation pulse trains (IPTs) labels, and the results are compared between different step sizes, noises, and model structures under the sliding time window with 120 sampling points. The U-Net based model got an accuracy greater than 94 % under simulated signals and 85 % under experimental signals, and identified more MUs than the structures based on convolutional neural network (CNN) and temporal convolutional network (TCN). The average latency of the U-Net based model is only 64 ms (a window duration time plus the prediction time) under the step size 20 data in both types of signals, and can be generalized to new data at different signal-to-noise (SNR). The efficiency of the proposed model is significantly higher than traditional methods such as gCKC. Meanwhile, the accuracy of the proposed model was not significantly different from the gCKC. In addition, the performance of the network under different step sizes of the sliding time window was verified. The experimental results indicate that the U-Net based model provides an efficient framework for blind source separation (BSS) of EMG signals, expanding the application of EMG signals in neural interaction.
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