Classification Of sEMG Signals Research Articles

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis. Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function. ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset. ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.

SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training.

Robot-assisted rehabilitation is a growing field that can provide an intensity, quality, and quantity of treatment that exceed therapist-mediated rehabilitation. Several control algorithms have been implemented in rehabilitation robots to develop a patient-cooperative strategy with the capacity to understand the intention of the user and provide suitable rehabilitation training. In this paper, we present an upper-limb motion pattern recognition method using surface electromyography (sEMG) signals with a support vector machine (SVM) to control a rehabilitation robot, ReRobot, which was built to conduct upper-limb rehabilitation training for post-stroke patients. For poststroke rehabilitation training using the ReRobot, the upper-limb motion of the patient's healthy side is first recognized by detecting and processing the sEMG signals; then, the ReRobot assists the impaired arm in conducting mirror rehabilitation therapy. To train and test the SVM model, five healthy subjects participated in the experiments and performed five standard upper-limb motions, including shoulder flexion, abduction, internal rotation, external rotation, and elbow joint flexion. Good accuracy was demonstrated in experimental results from the five healthy subjects. By recognizing the model motion of the healthy side, the rehabilitation robot can provide mirror therapy to the affected side. This method can be used as a control strategy of upper-limb rehabilitation robots for self-rehabilitation training with stroke patients.

A comparison of sEMG and MMG signal classification for automated muscle fatigue detection

This study compares the classification performance of both sEMG and MMG signal from fatiguing dynamic contraction of the biceps brachii. Commonly used statistical features are compared with a recently developed evolved pseudo-wavelet. Based on the literature, wavelet-based methods are a promising feature extraction technique for both types of signals (sEMG and MMG) during dynamic contractions. MMG results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 27.94 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05). For sEMG signals the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.96 percentage points when compared to other standard wavelet functions (p < 0.05), giving an average correct classification of 87.90%. The comparison demonstrates that for both the sEMG and the MMG signal, the feature giving best classification results was the evolved pseudo-wavelet.

A comparison of sEMG and MMG signal classification for automated muscle fatigue detection

This study compares the classification performance of both sEMG and MMG signal from fatiguing dynamic contraction of the biceps brachii. Commonly used statistical features are compared with a recently developed evolved pseudo-wavelet. Based on the literature, wavelet-based methods are a promising feature extraction technique for both types of signals (sEMG and MMG) during dynamic contractions. MMG results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 27.94 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05). For sEMG signals the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.45 percentage points to 14.96 percentage points when compared to other standard wavelet functions (p < 0.05), giving an average correct classification of 87.90%. The comparison demonstrates that for both the sEMG and the MMG signal, the feature giving best classification results was the evolved pseudo-wavelet.

SEMG signal classification with novel feature extraction using different machine learning approaches

sEMG signal classification with novel feature extraction using different machine learning approaches

Classification of sEMG Signals for the Detection of Vocal Fatigue based on VFI Scores.

In this new research, we expand on our previous system for vocal fatigue detection by adding five new features in the classifier. We also perform further testing on 37 test subjects. The goals were: 1) to classify subjects performing normal versus simulated pressed vocal gestures; 2) to distinguish vocally healthy from vocally fatigued subjects as determined by VFI score on factor 1; and 3) to determine the validity of the labels vis-a-vis the choice of this same VFI-factor-1 boundary. As the results demonstrated, the choice of classifier and the new features were quite appropriate, while there is margin for better choices of the VFI-factor-1 boundary.

Using Antonyan Vardan Transform and Extreme Learning Machines for Accurate sEMG Signal Classification.

In this paper, we present an evaluation of an adaptation of the Antonyan Vardan Transform (AVT) used in combination with an Extreme Learning Machines (ELM) classifier to process surface electromyography (sEMG) data used to classify six finger movements and a rest state. A total of 12 assays formed by three repetitions performed by four volunteers is analyzed. Additionally, a sample-by-sample output label comparison was performed to make a more comprehensive analysis of the system which was tested on a PC and embedded on a Rasp.berry Pi platform. Compared to literature papers, our system was capable to match or outperform similar solutions even using a simpler model, reaching mean accuracy rates above 94.

Feature Extraction and Classification of Hand Movements Surface Electromyogram Signals Based on Multi-method Integration

On the basis of analysing the characteristics of hand movement surface electrocardiogram electromyogram (sEMG) signals, we propose a feature extraction and classification method for hand movement sEMG signals based on a multi-method integration combining the wavelet, fractal and statistics methods. To start, the hand movement sEMG signals are de-noised by using the wavelet transform, the de-noised and reconstructed signals are decomposed, and the average high frequency coefficients in each scale space are calculated to constitute the feature vectors as the first part of the hand movement sEMG signals classification features. Next, according to the characteristics of hand movement sEMG signals and the classification needs, we analyse the multi-fractal spectrum of the de-noised and reconstructed signals at multiple scales and extract the relevant parameters of multi-fractal spectrum as the second part of the hand movement sEMG signals classification features. Then, according to the characteristics of hand movement sEMG signals, we extract the relevant statistical characteristics of sEMG signals as the third part of hand movement sEMG signals classification features. According to the extracted features, we use the Least Square Support Vector Machine and the Backpropagation neural network as classifiers to individually classify and combine the characteristics of hand movement sEMG signals and the experimental results. The final classification features are identified to accomplish the classification of hand movement sEMG signals. Finally, the advantages of the proposed method are illustrated by comparative analysis from multiple perspectives.

A novel approach for SEMG signal classification with adaptive local binary patterns.

Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

BackgroundSurface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.MethodsBoth sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.ResultsThe data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.ConclusionsThe results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.

A discriminant bispectrum feature for surface electromyogram signal classification

This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies–Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%.

Application of Higher Order Statistics to Surface Electromyogram Signal Classification

We propose a novel approach for surface electromyogram (sEMG) signal classification. This approach utilizes higher order statistics of sEMG signal to classify four primitive motions, i.e., elbow flexion, elbow extension, forearm supination, and forearm pronation. In documented research, the sEMG signal generated during isometric contraction is modeled by a stationary process whose probability density function (pdf) is assumed to be either Gaussian or Laplacian. In this paper, using Negentropy, we demonstrate that the level of non-Gaussianity of sEMG signal recorded in muscular forces below 25% of maximum voluntary contraction (MVC) is significant. Therefore, application of higher order statistics in sEMG signal processing is justified, due to the fact that more useful information can be extracted from the corresponding higher order statistics. An accurate classification is achieved by using the sequential forward selection (SFS) method for reducing of the dimensionality of feature space and the K-nearest neighbor (KNN) classifier. The results indicate that the proposed approach provides higher sEMG correct classification rates as compared to the existing methods.