Fig. 1

Block diagram of the data processing pipeline for ML-DRSNet training, validation, and testing. The MUSTs are extracted using the unsupervised CBSS algorithm from filtered, extended, and whitened HD-sEMG signals. The raw data is segmented with a sliding window to generate paired HD-sEMG segments and MUST labels. A five-fold cross-validation strategy is employed to divide the data segments into training, validation, and testing sets. All segments are z-score normalized using the mean and standard deviation of the training set before being input to the network. The binary cross-entropy loss between the predictions and the MUST labels is backpropagated to update the network. The model achieving the lowest validation loss is selected and evaluated on the testing set to assess its performance