A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand

Abstract

A multifunctional myoelectric prosthetic hand is a perfect gift for an upper-limb amputee, however, the myoelectric control for a prosthetic hand is not so good now. Here, the paper presents a comparative study on electromyography (EMG) pattern recognition based on PCA and LDA for an anthropomorphic robotic hand. Four channels of surface EMG (sEMG) signals were recorded from the subject's forearm. Time-domain analysis, frequency-domain analysis, wavelet transform analysis, nonlinear entropy analysis and fractal analysis were done and fourteen kinds of features were extracted from sEMG signals. The features were divided into four groups, and the performances of the four groups were compared and analyzed. In the feature projection stage, three schemes were proposed and their performances were compared with each other. The first one only used the principal component analysis (PCA) for dimension reduction. And the second one only used the linear discriminant analysis (LDA) for dimension reduction. The third one used PCA for the first step of dimensionality reduction, and then used LDA for the next step of dimensionality reduction. In the classification stage, minimum distance classifier (MDC) was employed for identifying nine kinds of hand/wrist motions in the projected space. Comparative experiments of four groups of features and three projection schemes were done and evaluated. The online experiment of real-time myoelectric control for an anthropomorphic robotic hand was done as well.