Gaussian Smoothing Filter for Improved EMG Signal Modeling

This paper considers the problem of classifying human hand gestures by using electromyography (EMG) signals that are usually corrupted with noise. Noisy EMG signals result in significant degradation of classification performance and to enhance the performance, a Gaussian Smoothing Filter (GSF) is employed to remove the noise in the sensed EMG signals. The filtered signals, along with various classification schemes, are used to classify several hand gestures. The features of the GSF include: high filtering efficiency, simple implementation, and equal support in frequency and time domains, endowing the GSF with the ability to filter out the noise while partially retaining high frequency components of the original signal. The use of GSF produces smoothed EMG signals that not only enhances the classification accuracy but also reduces the computational time required to develop and test the classifiers. Experiments are conducted on EMG signals, captured from a MYO band, using multiple classification techniques and a significant improvement is observed in the classification performance when using the GSF to filter out the noise in the EMG signals. The classification performance for the EMG signals, for both unfiltered and filtered cases, is compared and the use of GSF is shown to yield significant performance enhancement. Moreover, a significant reduction in the computational time is reported when employing the GSF-based classification, demonstrating the advantages of the GSF for classifying EMG signals. Finally, a comparison is performed for classifying the EMG signals smoothed using a Median Filter (MF) versus the GSF and the superiority of the GSF is shown.

Human walking is an important and intuitive operation in daily life, but it is different for people missing this ability. Recognizing human gait cycle portions during walking is very useful for understanding the biomechanics of the muscles, pre-disease’s diagnosis, and designing lower limb prosthetics. The normal gait cycle is divided into stance and swing phases. In this work, benchmarking of several classification techniques is performed, based on electromyography (EMG) data collected from seven lower limb muscles, with the gait cycle phase used as a target vector to label the EMG data. The dataset is split into a training set used to build a statistical model using a specified classification technique, and a test set for purpose of testing a pre-generated statistical model. EMG signals are unfortunately normally corrupted with noise, which drastically reduces their classification performance. A median filter and root mean square (RMS) filter were thus applied to the raw EMG signals, and the performance of the classification techniques calculated in all cases for the raw EMG, EMG with median filter, and EMG with RMS filters for comparison purposes. The use of filters offered good enhancement in the classification process. The median filtered EMG signal performed best and gave higher accuracy than the raw EMG and RMS filtered EMG signals due to the technique’s efficacy in removing outliers. This work also offers a simple explanation of each classifier algorithm used, and a graphical feature selection was also applied to all seven muscles of interest to identify the muscle with the most influence on the gait cycle. The Rectus Femoris muscle shows the best activity separation between swing and stance phases, working mainly during the swing phase; this higher activity during the swing phase thus caused it to be identified as the muscle with the most influence over the normal gait cycle.

In this paper, a novel bacterial foraging algorithm (BFA)-Gaussian support vector classifier machine (GSVCM) model was proposed to improve the fatigue classification accuracy of electromyography (EMG) signals. This optimization mechanism involves the kernel parameter setting in the GSVCM training procedure, which significantly influences the classification accuracy. Experiments were conducted based on the EMG signal to differentiate the normal and fatigue status. In the proposed method, the EMG signals were decomposed into intrinsic mode functions by ensemble empirical mode decomposition (EEMD) before the mean instantaneous frequency could be obtained by Hilbert transform (HT). Finally, the fatigue statistical features can be extracted from fast Fourier transform and EEMD—HT. The application of this model to the fatigue status recognition of EMG signal indicated that further significant enhancement of the classification accuracy can be achieved by the proposed BFA-GSVCM classification system. The diagnostic method is effective and feasible.

Relationships of Vibromyographic and Electromyographic Signals During Isometric Voluntary Contraction

Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).

One of the most important electrophysiological signal is the Electromyography (EMG) signal, which is widely used in medical and engineering studies. This signal contains a wealth of information about muscle functions. Therefore, the EMG signal is becoming increasingly important and has started to be used in many applications like finger movement rehabilitation. However, an advanced EMG signal analysis method is required for efficient usage of such applications. This signal analysis can include signal detection, decomposition, processing, and classification. There are many approaches in studying the EMG signals, however, one of the important factor of analyzing is to get the most efficient and effective features that can be extracted from the raw signal. This paper presents the best feature extraction set compared to previous studies. Where eighteen well-known features algorithm has been tested using the sequential forward searching (SFS) method to get excellent classification accuracy in a minimum processing time. Among these novel features only four combinations have been selected with perfect results, which are; Hjorth Time Domain parameters (HTD), Mean Absolute Value (MAV), Root Mean Square (RMS) and Wavelet Packet Transform (WPT). The superiority of this feature set has been proven experimentally, and the results show that the classification accuracy could reach up to 99% to recognize the individual and combined for ten classes of finger movements using only two EMG channels.

Automatic analysis of motor unit potentials

Electromyography (EMG) signals have been successfully employed for driving prosthetic limbs of a single or double degree of freedom. This principle works by using the amplitude of the EMG signals to decide between one or two simpler movements. This method underperforms as compared to the contemporary advances done at the mechanical, electronics, and robotics end, and it lacks intuition to perform everyday life tasks. Recently, research on myoelectric control based on pattern recognition (PR) shows promising results with the aid of machine learning classifiers. Using the approach termed as EMG-PR, EMG signals are divided into analysis windows, and features are extracted for each window. These features are then fed to the machine learning classifiers as input. By offering multiple class movements and intuitive control, this method has the potential to power Prosthesis an amputated subject to perform everyday life movements. In this paper, we investigate the effect of the analysis window and feature selection on classification accuracy of different hand and wrist movements using time-domain features. We show that effective data preprocessing and optimum feature selection helps to improve the classification accuracy of hand movements. We use publicly available hand and wrist gesture dataset of 40 intact and 11 trans-radial amputated subjects for experimentation. Results computed using different classification algorithms show that the proposed preprocessing and features selection outperforms the baseline and achieve up to \(98\%\) classification accuracy for both intact and trans-radial subjects.

<p>The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at force levels up to 20% of maximum voluntary contraction (MVC) it is required: (i) To identify the MUAPs composing the EMG signal, (ii) To classify MUAPs with similar shape and (iii) To decompose the superimposed MUAP waveforms into their constituent MUAPs. For the classification of MUAPs two different pattern recognition techniques are presented (i) An artificial neural network (ANN) technique based on unsupervised learning using the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and (ii) A statistical pattern recognition technique based on the euclidian distance. The success rate on real data for the ANN technique is about 96% and for the statistical one about 94%. For the decomposition of the superimposed waveforms the following technique is used: (i) Cross-correlation of each of the unique MUAP waveforms, obtained by the classification process with the superimposed waveforms in order to find the best matching point and (ii) A combination of euclidian distance and area measures in order to classify the components of the decomposed waveform. The success rate for the decomposition procedure is about 90%.</p>

The shapes and sounds of isolated motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. These parameters can be analyzed qualitatively by an expert or quantitatively by using pattern recognition techniques. Due to the advantages of quantitative EMG method, developing robust automated MUAP classifiers have been explored and several systems have been developed for this purpose by now, but the accuracy of the existing methods is not high enough to be used in clinical environments. In this paper, a novel classification strategy based on ensemble of support vector machines (SVMs) classifiers in hybrid serial/parallel architecture is proposed to determine the class label (myopathic, neuropathic, or normal) for a given MUAP. The developed system employs both time domain and time-frequency domain features of the MUAPs extracted from an EMG signal using an EMG signal decomposition system. Different classification strategies including single classifier and multiple classifiers with several subsets of features were investigated. Experimental results using a set of real EMG signals showed robust performance of multi-classifier methods proposed here. Of the methods studied, the multi-classifier that uses multiple features sets and a combination of both trainable and nontrainable fusion techniques to aggregate base classifiers showed the best performance with average accuracy of 97% which is significantly higher than the average accuracy of single SVM-based classifier system (i.e., 88%).

A novel high-precision decomposition algorithm for EMG (electromyography) signals based on the Viterbi algorithm is described. Under certain assumptions this algorithm yields the least squares solution in the time domain. It is shown that the EMG signal source can be modeled as a (M+1)-ary signaling system with intersymbol interference. Direct application of the Viterbi algorithm would lead to a decoder with approximately=10/sup 20/ states, which cannot be implemented. It is shown that according to the special nature of the EMG signal essential simplifications can be done. The resulting algorithm requires only on the order of 10/sup 4/ states. The test of the implemented algorithm shows that, in the mean, over 90% correctly detected innervation time points and only about 5% additional time points can be expected.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper aims to propose a systematic feature extraction technique for classification of Neuromuscular diseases. In this proposed scheme, modified CENTRIST (CENsus TRansform hISTogram) for 1-dimensional signals is introduced as the feature descriptor for the given Electromyography (EMG) signals from two different publically available databases. The proposed algorithm encodes discriminative structural properties of the EMG signal of different classes and is vigil in accounting abrupt transitions in the signal. K-nearest neighborhood (KNN) and Support Vector Machine (SVM) classifiers are employed for the purpose of classification. The crux of this paper is to formulate a feature extraction technique to distinguish the structural and topological properties of Normal, Myopathic, Neuropathic and Amyotrophic Lateral Sclerosis (ALS) EMG signals. The classification performance was experimentally verified for a 2-class problem and a 3-class problem using standard databases and the results revealed 95% classification accuracy.

Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.

We present a framework for the detection of swing and stance phases of gait cycle during walk on a perturbing platform, i.e., a slackline. The framework employs a time varying Kalman filter to reconstruct neural commands from the accelerometer data acquired from slackline perturbations that are generated by the participants. Electromyography (EMG) signals reconstructed from the estimated neural command were compared with surface EMG recordings during slackline walking. For four gait trials, the EMG signals were reconstructed with 92%-95% accuracy. These reconstructed signals were dimensionally reduced using principal component analysis (PCA) and classified using support vector machine (SVM), Decision Tree (DT), Naive Bayes (NB) classifiers. The stance and swing phases were discriminated, the performance of each classifiers was evaluated and classification accuracy was later enhanced with post processing. The DT algorithm outperformed others in detecting the stance and swing phases of gait cycle on slackline with 99.5% accuracy.

Electromyography (EMG) signal is an electrical indicator for neuromuscular activation. It provides direct access to physiological processes enabling the muscle to generate force and produce movement in order to accomplish countless functions. As a successful classification of the EMG signal is basically dependent on the selection of the best parameters carefully, this paper proposes a hybrid optimized classification model for EMG signals classification. The proposed system implements grey wolf optimizer (GWO) combined with support vector machines (SVMs) classification algorithm in order to improve the classification accuracy via selecting the optimal settings of SVMs parameters. The proposed approach consists of three phases; namely pre-processing, feature extraction, and GWO-SVMs classification phases. The obtained experimental results obviously indicate that significant enhancements in terms of classification accuracy have been achieved by the proposed GWO-SVMs classification system. It has outperformed the typical SVMs classification algorithm via achieving an accuracy of over 90 % using the radial basis function (RBF) kernel function.