Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

Abstract

The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control. To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE). First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment. Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set. Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements. Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set. The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.

Electromyography Signal Analysis for the Detection of TMJ Disorder Using Classification Models and Multivariate Analysis

“…Wavelet based features reduces the dimension by spectral analysis and has improved the classification in detecting the TMJ disorder. After applying the extracted and scaled features to different classifiers like logistic regression (LR) [13], Linear discriminant analysis (LDA) [12] and support vector machine (SVM) [11] SVM is giving the better accuracy. A summary of SEMG classification technique is shown in Table 1.…”

“…The use of the electromyography (EMG) in various medical domains has been widely used. It is commonly used for the diagnosis of various disorders such as neuromuscular disorder and myopathy [11].…”

“…The inclusion criteria were based on the participant's informed consent. The exclusion criteria included a history of psychological or medical treatment, previous surgical procedures, and a history of orthodontic treatment for the jaws [11]. Sample size is based on the root mean square and the electromyographic signals from the anterior bundles and masseter muscles with α=0.05, β=0.20 and 80% test power [8].…”

“…Bandage tape was utilized to secure the electrodes [15]. The distance between the two electrodes was 20 mm, which is as suggested by an international organization [11]. Figure 1 shows the raw electromyographic signals during maximal voluntary clenching.…”

Feature Fusion-Based Improved Capsule Network for sEMG Signal Recognition

“…Surface electromyogram (sEMG), an electrical signal generated on skin surface during muscle contraction, contains rich information about muscle activity and can be used to identify the subject's action intention [1]. us, it is commonly used in rehabilitation, intelligent bionics, and human-computer interaction [2].…”