Research on Denoising Method of Upper-Limb sEMG Signal Based on HO-TVF-EMD and IMF Energy Entropy Increment

To tackle the low signal-to-noise ratio (SNR) of surface electromyography (sEMG) signals during upper-limb rehabilitation training—an issue arising from noise interference that undermines downstream motion recognition—this work develops a novel denoising approach integrating the Hippopotamus Optimization algorithm (HO) and time-varying filtering empirical mode decomposition (TVF-EMD). The HO algorithm optimizes the adaptive parameters of TVF-EMD to decompose raw sEMG into multiple intrinsic mode functions (IMFs); energy entropy increment is further adopted to separate valid signal components from noisy IMFs, and only effective modes are reconstructed to obtain clean sEMG signals. Comparative experiments reveal that the proposed HO-TVF-EMD improves SNR by over 7.48% on average and reduces root mean square error (RMSE) by more than 7.08% relative to VMD, vanilla TVF-EMD, and TVF-EMD optimized by IGOA, ISSA and SMA. The denoised waveforms remain smooth while preserving the essential signal characteristics. Within the evaluated dataset collected from six healthy participants, the corresponding motion recognition accuracy reached 92.34%, exceeding those of the benchmark approaches by 15.67%, 14.00%, 5.67%, 4.01%, and 7.34%, respectively. Overall, the proposed HO-TVF-EMD framework provides an effective denoising solution for improving signal quality and subsequent motion recognition performance. The results indicate its potential for future rehabilitation-oriented myoelectric applications, while further validation using larger populations and complete closed-loop systems remains necessary.