WD-EEMD based Voting Classifier for hand gestures classification using sEMG signals

Singh, Puru Lokendra; Verma, Samidha Mridul; Vijayvargiya, Ankit; Kumar, Rajesh

doi:10.1109/iccca52192.2021.9666291

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…These IMFs are subsequently filtered or thresholded to eliminate any remaining noise in the low frequency of the signal, which is essential to eliminate noise and enhance the signal. The hybrid approach of wavelet denoising and EEMD has been effectively applied to denoise an EMG signal for a variety of activities, including lower limb activities, and upper limb activities [34,35]. In certain circumstances, this technique can be more effective than wavelet denoising or EEMD alone, despite requiring additional computational resources [34].…”

Section: Plos Onementioning

confidence: 99%

“…At the end of decomposition, a single approximate coefficient is obtained from the last level and four detailed coefficients are obtained from all four levels of decomposition to reconstruct the signal. Garotte thresholding is applied with a universal selection rule on the second detail coefficient level (D2) [35]. After that, a reconstructed signal from WD is processed with EEMD which decomposes the signal further into a set of IMFs with a residual.…”

Section: Plos Onementioning

confidence: 99%

“…In such cases, denoising techniques that involve wavelet decomposition [23], empirical mode decomposition [32], or independent component analysis [33] can prove to be essential for the minimization of noise. In previous studies [34,35], the authors introduced a mix of denoising methodologies known as WD-EEMD (Wavelet Decomposition with Ensemble Empirical Mode Decomposition). It assists in the examination of sEMG signals during the identification of lower limb and upper limb activities.…”

Section: Introductionmentioning

confidence: 99%

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S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

Vijayvargiya,

Sinha,

Gehlot

et al. 2024

PLoS ONE

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The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

Vijayvargiya,

Sinha,

Gehlot

et al. 2024

PLoS ONE

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“…These limitations lead to a high sensitivity of the EMD to sampling and noise. Several EMD variations have been proposed as possible answers to the mode mixing issue, and the inclusion of ensemble empirical mode decomposition (EEMD) has been documented [15]. The authors conducted a comprehensive ensemble empirical mode decomposition (CEEMD) [16].…”

Section: Introductionmentioning

confidence: 99%

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

Rani,

Hashmi,

Muhammad

2024

IEEE Access

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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.

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sEMG-Based Classification of Finger Movement with Machine Learning

Gehlot,

Jena,

Vijayvargiya

et al. 2023

2023 International Conference on Computer, Electronics &Amp; Electrical Engineering &Amp; Their Applications (IC2E3)

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WD-EEMD based Voting Classifier for hand gestures classification using sEMG signals

Cited by 5 publications

References 19 publications

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

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

sEMG-Based Classification of Finger Movement with Machine Learning

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