Abstract
Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.
Highlights
Electromyography (EMG) signals recorded from the residual muscles have the potential to be used as a control source for assistive rehabilitation device and myoelectric prosthetic [1]
To evaluate the effectiveness of proposed method, competitive binary grey wolf optimizer (CBGWO) is compared with Binary Grey Wolf Optimization Model 1 (BGWO1), BGWO2, binary particle swarm optimization (BPSO), and genetic algorithm (GA)
K-nearest neighbor (KNN) with k = 1 is used as the learning algorithm, due to its speed and simplicity [16,28]
Summary
Electromyography (EMG) signals recorded from the residual muscles have the potential to be used as a control source for assistive rehabilitation device and myoelectric prosthetic [1]. EMG is a bioelectrical signal that offers rich muscle information, which can be used to identify and recognize hand motions [2]. The development of EMG-based rehabilitation devices is becoming of major interest to many biomedical researchers. Most researchers have applied advanced signal processing, feature extraction, machine learning, and feature selection algorithms to enhance the performance of the EMG pattern recognition system [4,5,6,7]. Machine learning acts as the classifier, to classify the features for recognizing the hand movements
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