Improvement of EMG Pattern Recognition Model Performance in Repeated Uses by Combining Feature Selection and Incremental Transfer Learning

Abstract

Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p < 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p < 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses.

A Novel Method for Hand Movement Recognition Based on Wavelet Packet Transform and Principal Component Analysis with Surface Electromyogram

“…The feature extraction of sEMG signals is a key step in the classification of hand movements based on sEMG signals [ 40 ]. In this work, four features are extracted from the sub-band signals obtained by decomposing the sEMG signal by WPT to classify hand movements [ 41 , 42 ]. These features were selected on the basis of previous studies that showed their usefulness in distinguishing hand movements based on sEMG signals [ 2 , 43 ].…”

“…Feature extraction is to convert the sEMG signal into a compact and information-rich feature space. The sEMG signal feature extraction methods are generally divided into time-domain (TD) features and frequency-domain (FD) features [ 41 ]. The TD feature is the TD statistics obtained by directly performing statistical analysis on the signal amplitude [ 42 ].…”

“…The FD features are extracted from the frequency spectrum of the sEMG signal [ 47 ]. According to the research of hand movement classification based on sEMG signals, the FD features extracted from sEMG signals mainly include median frequency (MDF), mean frequency (MNF), and mean power (MNP) [ 41 ]. Phinyomark et al [ 48 ] proposed two modified FD features for robust feature extraction.…”

A survey on bio-signal analysis for human-robot interaction

“…Following that, a specific tool can be chosen based on the system's requirements and characteristics. One of the most widely used pattern detection methods in myoelectric signals is support vector machines (SVM), which discover an n-dimensional hyperplane that can divide a set of extracted input features into different classes [71].…”

Research on upper limb rehabilitation system based on visual feedback

“…A self-collected dataset similar to the article [10] was used in this study. There are ten healthy subjects in this dataset and each individual was selected for 7 signal acquisition points in the upper limb, which could be seen as Figure 3(a): the anterior deltoid, middle deltoid, posterior deltoid, biceps, triceps, brachioradialis and flexor muscles.…”