An Enhanced Random Convolutional Kernel Transform for Diverse and Robust Feature Extraction from High-Density Surface Electromyograms for Cross-day Gesture Recognition

High-density surface electromyogram (HD-sEMG) has become a powerful signal source for hand gesture recognition. However, existing approaches suffer from limited feature diversity in hand-crafted methods and high data dependency in deep learning models, necessitating individual model calibration for each user due to neuromuscular differences. We propose EMG-ROCKET, an enhanced version of the RandOm Convolutional KErnel Transform (ROCKET), designed to extract diverse and robust HD-sEMG features without prior knowledge or extensive training. EMG-ROCKET integrates random channel fusion and enhanced aggregation functions to enhance robustness against cross-day signal variability in HD-sEMG applications. In cross-day evaluations of hand gesture recognition, a Ridge classifier using EMG-ROCKET features achieved 84.3% and 77.8% accuracy on two HD-sEMG datasets, outperforming all baseline methods. Furthermore, feature contribution analysis demonstrates the capability of EMG-ROCKET to capture spatial muscle activation patterns, offering insights into motion mechanisms. These results establish EMG-ROCKET as a promising, training-free solution for robust HD-sEMG feature extraction, facilitating practical human-machine interaction applications.