A novel pipeline for converting surface electromyography signals into muscle activations

Résumé fourni par la source

This study introduces a novel pipeline for converting surface electromyography (sEMG) signals into muscle activations using the Hilbert-Huang Transform. Traditional approaches in this context often apply low-pass filters that suppress high-frequency components, potentially discarding physiologically relevant signal information. In contrast, the proposed method leverages Empirical Mode Decomposition and Hilbert spectral analysis to preserve the nonstationary and multi-frequency nature of sEMG data. Activation outputs are then mapped through physiologically inspired dynamics, yielding time-resolved muscle activations. Comparative analyses were conducted across three muscles (EDC, FDS, FDP) using data from 10 subjects each performing 5 cylindrical grasps. Intra-subject comparisons using Wilcoxon signed-rank tests revealed statistically significant improvements (p < 0.001) in nearly all trials. Linear mixed-effects analysis of log-transformed activations showed that the new pipeline yields significantly higher muscle activations within each muscle: EDC GMR = 1.31 (95% CI: 1.255–1.359), FDS GMR = 1.35 (95% CI: 1.296–1.396), and FDP GMR = 1.29 (95% CI: 1.248–1.342), all p < 0.001. These results suggest that the choice of sEMG processing pipeline can meaningfully alter activation estimates and potentially influence musculoskeletal model estimation. The method presented provides a robust and physiologically consistent alternative for applications in biomechanics, prosthetic control, and neuromuscular modelling.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.