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

Ankit Vijayvargiya, Vishu Gupta, Rajesh Kumar, Nilanjan Dey, Joao Manuel R.S. Tavares

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)20431-20439
Number of pages9
JournalIEEE Sensors Journal
Volume21
Issue number18
DOIs
Publication statusPublished - 15 Sept 2021
Externally publishedYes

Keywords

  • Biomedical signal analysis
  • EMG classification
  • ensemble empirical mode decomposition
  • gait activities
  • linear discriminant analysis
  • wavelet denoising
  • WD-EEMD

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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