Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal

Abstract

Surface Electromyography (sEMG) signal classification and analysis have attracted particular attention because of their many biological applications. These can be utilized for kinesiological research, motion intention recognition, human-machine interaction, rehabilitation, and disease diagnosis. Due to the interference of noise, these signals are challenging to process. To attenuate noise interference from sEMG signals, a denoising method based on Variational Mode Decomposition (VMD) with selected Intrinsic Mode Functions (IMFs) based on Correlation Coefficient (CC) and Empirical Wavelet Transform (EWT) is proposed in this study. The suggested method applies the EWT to the specific IMFs obtained by VMD, decomposing the raw signal with noise into several IMFs based on correlation coefficients. To increase the signal separability, stability and to extract the low-frequency information of the signal, apply EWT on selected IMFs; it decomposes IMFs into the multiresolution analysis (MRA). Following segmentation, 16 Features are recovered using the time domain, frequency domain, and entropy with an overlapping window technique of 250ms and 50% overlap. Later, four machine learning classifiers— Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Naive Bayes (NB), and Ensemble Subspace k-nearest Neighbor (KNN) — are used to identify the three lower limb movements. The results obtained for an Ensemble Subspace KNN classifier have a 98.9% accuracy, a 98.8% F1-Score, and a 98.7% sensitivity. Lastly, machine learning (ML) is used in this work to create a new feature extraction technique for lower limb activity prediction based on VMD-EWT with CC.

Keywords

• Electromyography (EMG)
• variational mode decomposition (VMD)
• empirical wavelet transform (EWT)
• machine learning (ML)