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.

Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

“…Zhang uses image processing algorithms to identify multiple gestures [9] . Additional, many researchers are making efforts on analyzing sEMG signals [10][11][12][13] . To summarize, most of the above studies are based on pattern recognition (PR).…”

Research on rehabilitation training bed with action prediction based on NARX neural network

“…According to the definition of interclass dispersion matrix and intraclass dispersion matrix, we can get: …”

Expert committee classifier for hand motions recognition from EMG signals

“…In [5] mention works that have used several methods in both time and frequency domain getting good results in the EMG pattern recognition. In this paper statistical analysis methods such as mean, variance, energy, maximum value and relations between features were used in the time and frequency processing techniques for each channel or muscle.…”

“…Two techniques for the analysis and feature extractions were applied to address the problem and achieve a reduction in the dimensionality of the vector: Covariance matrix (MC) between features were performed and Principal Component Analysis (PCA) which in [5] had good performance in the results obtained. Figure 5 shows the results of the covariance matrices calculated in (a) for the 4 movements to classify, in (b) for two movements (flexion and extension of the wrist) and (c) for both remaining motions (opening and closing the hand).…”

“…To address these challenges there are several commercial prosthetic devices, developed in other countries, such as Otto Bock hand [2], the i-Limb [3] or SmartHand [4] that allow these people to recreate some of the human hand movements [5].…”