Research on the recognition model of exercise fatigue based on the fusion of sEMG and ECG signals.

ABSTRACT: This study significantly enhances the accuracy of exercise state identification in wearable devices through improved denoising techniques for sEMG and ECG signals. By adopting an optimized Variational Mode Decomposition (VMD) method, combined with the Improved Sparrow Search Algorithm and Second Generation Wavelet Transform (ISSA-VMD-SWT), and introducing chaos mapping to strengthen the algorithm's initial population, this approach effectively reduces noise while preserving key fatigue-related features. In tests conducted on data from 32 participants, the method achieved accuracy rates of 93.25%, 95.16%, and 93.05% for identifying "Easy," "Transition," and "Tired" exercise states, respectively, showing significant advantages over traditional denoising techniques. These results indicate tha

SUBMITTER: Li H 

PROVIDER: S-EPMC10951635 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature