Classification of Gesture Electromyography by Dynamic Mode Decomposition

Abstract

Accurate classification of hand gestures from surface electromyography (sEMG) signals is essential for human-computer interaction and myoelectric prosthetic control, yet conventional feature-extraction methods struggle to capture the temporal dynamics of muscle activation. Dynamic Mode Decomposition (DMD), a data-driven technique originally developed for fluid dynamics, has not been extensively validated for sEMG signals. This study addresses that gap by introducing DMD as a feature-extraction framework for sEMG-based gesture classification, providing a mathematically rigorous approach for capturing spatiotemporal muscle-activation patterns. Using sEMG recordings from 37 participants, we extracted DMD-reconstructed features–Mean Absolute Value (MAV), Root Mean Square (RMS), Simple Square Integral (SSI), Variance (VAR), Standard Deviation (STD), Median, Integrated EMG (IEMG), and spectral features–and compared their discriminative power against traditional features across multiple classifiers. DMD-derived features significantly distinguished common hand gestures ( $p \lt 0.05$ , Bonferroni-corrected) and consistently outperformed traditional features: MAV, RMS, SSI, and VAR achieved 96% accuracy with k-nearest neighbors and 88% with random forest, while support vector machines reached 97% and 96% accuracy using STD, MAV, and RMS, respectively. Convolutional Neural Networks and Deep Stacked Neural Networks achieved up to 85% and 87% accuracy, respectively, using DMD-extracted spectral features. These results establish DMD as an effective, interpretable feature-extraction method for sEMG-based gesture classification, with applications in rehabilitation engineering, prosthetic control, and human-computer interaction.