Single channel EMG-based continuous terrain identification with simple classifier for lower limb prosthesis

Abstract

The focus of the present research endeavour is to propose a single channel Electromyogram (EMG) signal driven continuous terrain identification method utilizing a simple classifier. An iterative feature selection algorithm has also been proposed to provide effective information to the classifiers. The proposed method has been validated on EMG signal of fifteen subjects and ten subjects for three and five daily life terrains respectively. Feature selection algorithm has significantly improved the identification accuracy (ANOVA, p-value<0.05) as compared to principal component analysis (PCA) technique. The average identification accuracies obtained by Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Neural Network (NN) classifiers are 96.83±0.28%, 97.45±0.32% and 97.61±0.22% respectively. Subject wise performance (five subjects) of individually trained classifiers shows no significant degradation and difference in performance among the subjects even for the untrained data (ANOVA, p-value>0.05). The study has been extended to dual muscle approach for terrain identification. However, the proposed algorithm has shown similar performance even with the single muscle approach (ANOVA, p-value>0.05). The outcome of the proposed continuous terrain identification method shows a pronounced potential in efficient lower limb prosthesis control.