A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Mechanomyography (MMG) signals have extensive applications in muscle function assessment and human intention recognition. However, during signal acquisition, MMG are easily contaminated by noise artifacts, which seriously affects the recognition of their characteristics. To address these issues, a novel suppression artifact removal method based on recursive least square (RLS), improved Gray Wolf Optimizer-optimized variable mode decomposition (IGWO-VMD), complete ensemble empirical with adaptive (CEEMDAN) is proposed. In this paper, RLS algorithm first applied to adaptively filter out power line interference (PLI). Then, IGWO designed select appropriate VMD parameters use decompose noisy into band-limited intrinsic functions (BLIMFs). addition, BLIMFs classified low-frequency part high-frequency according given correlation coefficient (CC) threshold value. The effective components identified center frequency. Meanwhile, decomposed CEEMDAN, its obtained proposed sample entropy range. Finally, low parts reconstructed obtain denoised realize extraction useful signals. Simulation experiment results demonstrate that outperforms classical methods IGWO-VMD terms denoising performance. effectiveness verified through measured experiments. not only effectively suppresses artifacts but also overcomes limitations CCEMDAN.