A Hybrid WD-EEMD sEMG Feature Extraction Technique for Lower Limb Activity Recognition

Abstract

Classification and analysis of surface EMG (sEMG) signals have been of particular interest due to their numerous applications in the biomedical field. They can be used for the diagnosis of neuromuscular diseases, kinesiological studies, and human-machine interaction. However, these signals are difficult to process due to their noisy nature. To overcome this problem, a hybrid of wavelet with ensemble empirical mode decomposition pre-processing technique called WD-EEMD is proposed for classifying lower limb activities based on sEMG signals in healthy and knee abnormal subjects. First, Wavelet De-noising is used for filtering out white Gaussian Noise (WGN) and unwanted signals (contribution of other muscle signals). Next, an Ensemble Empirical Mode Decomposition is used for filtering out power line interference (PLI) and baseline wandering (BW) noises, followed by extraction of a total of nine time-domain features. Finally, the performance parameters of the Linear Discriminant Analysis (LDA) classifier are calculated with a 3-fold cross-validation technique. This study involves 11 healthy and 11 individuals with a knee abnormality for three different activities: walking, flexion of the leg up (standing), and leg extension from sitting position (sitting). Different pre-processing techniques similar to that of WD-EEMD were compared. It was observed that the proposed method achieves an average classification accuracy of 90.69% and 97.45% for healthy subjects and knee abnormal subjects, respectively.

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals : A Review

“…These results appear to be consistent with the literature, since most studies have reported using either the db2 [73][74][75][76][77][78][79] or the db4 [67,[80][81][82][83][84]. In terms of optimal decomposition level, level 4 appears to be the most widely used [5,19,71,77,82,85,86], while the level 3 is also frequently chosen [25,72,[87][88][89][90].…”

“…For example, [58] used wavelet thresholding to remove the remaining noise from its ANC. Same for [19] which uses WT to remove WGN and then EEMD for PLI and BW. Moreover, it is common to apply a band-pass filter (10Hz-500Hz) before the proposed denoising method [106].…”

“…Contamination reduction methods always represent a compromise between removing contaminants and preserving the EMG information [10]. The amplitude of a typical sEMG signal is between 0 and 10 mV across a frequency range of 10 to 500Hz [19]. Since each contaminant has its own amplitude and frequency characteristics, the methods employed to reduce individual contaminants will vary accordingly.…”

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

“…[ 101 ] Ensemble EMD was put to solve the shortage of bad performances in complicated spatial and temporal structures noises eliminating. [ 106 ] S. Ma et al used a variable mode decomposition (VMD)‐based filter to denoise PLI, BW, and GWN, achieving a relatively low RMSE with the same SNR. [ 101 ] M. Mortezaee et al use singular spectrum analysis to eliminate ECG interference, whose method needs no parameters and is model‐free, which can be applied to decompose slowly time‐varying signals.…”

Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition

“…Te use of wavelet decomposition [34,35] has seen a rising trend in sEMG signal denoising for both the upper and lower limbs. Tis is because it can efectively eliminate the white Gaussian noise from the signal.…”