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

Publication date: Available online 20 July 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Rohit Gupta, Ravinder AgarwalAbstractThe 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 (ten men, five women) for three daily life terrains. Feature selection algorithm has significantly improved the identification accuracy (p-value < 0.05) as compared to principal component analysis (PCA) technique. The best identification accuracies obtained by support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) classifiers are 97.89 ± 1.21%, 98.39 ± 1.43% and 99.06 ± 0.87% 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 (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 (p-value > 0.05). The outcome of the proposed continuous terrain identification method shows a pronounced ...

Source: Biocybernetics and Biomedical Engineering - July 21, 2019 Category: Biomedical Engineering Source Type: research