High-precision EMG signal decomposition with modified Viterbi algorithm

Relationships of Vibromyographic and Electromyographic Signals During Isometric Voluntary Contraction

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.

Objective: To investigate the characteristics of electromyography (EMG) signals and the starting threshold voltages of the orbicularis oris muscles (OOM) in healthy rhesus monkeys under different muscle movement conditions. Methods: The EMG signals and the starting threshold voltages at different time points in 4 healthy rhesus monkeys were acquired and recorded with EMG device and evoked potentiometer. The voltage amplitude variation of EMG signals was analyzed, and the voltage amplitude range of EMG signals at the beginning of OOM contraction was established. The data were statistically analyzed by one-way ANOVA. Results: The EMG of OOM in healthy monkeys in the quiet, natural and continuous mouth-closed state was linear and relatively stable, and the absolute value fluctuated between 15 and 50 μV. The EMG waveform increased rapidly during the natural lip contraction movement, and its amplitude fluctuated greatly, with the highest absolute value of the peak value reaching hundreds of microvolts. The amplitude of EMG induced by continuous mouth closure was more than thousands of microvolts. There was no significant difference in EMG amplitudes of OOM in the healthy rhesus monkey under quiet and continuous lip closure at different time points (P>0.05). There was no significant difference in threshold voltages in the state of natural lip contraction of bilateral OOM at different time points (average range: 57.17-57.47 μV) in the healthy rhesus monkeys (P>0.05). There was no significant difference in threshold voltages of OOM induced by bilateral OOM at different time points(average range: 55.38-55.99 μV) in the healthy rhesus monkeys(P>0.05). There were significant differences in the absolute values of EMG amplitudes of OOM between the three lip movement modes: (30.67±8.72) μV in quiet and natural continuous lip closure (475.12±54.72) μV in natural lip contraction, and (921.22±312.79) μV in the induced persistent lip closure, with t values of -8.48, -9.35 and -5.01 respectively, all P<0.001. Conclusions: The EMG signals of OOM show different characteristics under different muscle movement conditions, which can be used as a basis for computer to judge and recognize the movement conditions of OOM. The upper limits of the EMG threshold voltage values of OOM under different motion states are 55-60 μV.

Identification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.

The goal of the research presented in this chapter is to improve the classification process of electromyography (EMG) signals that are contaminated with noise. If the existence of noise in EMG signals is not accounted for, it can degrade the performance of the classification task. Therefore, it is necessary to utilize an efficient filtering process to improve the classification of EMG signals. Guided by the need to filter the noise out of EMG signals, this chapter proposes to employ a Gaussian smoothing filter (GSF) that is simple in its implementation with an efficient filtering performance. The GSF, which is a Gaussian function, offers equal support in both frequency and time domains, allowing it to yield a performance compromise in removing the noise while preserving high frequency components of EMG signals. It is additionally shown that the use of GSF not only enhances the classification accuracy, but it also reduces the computational time needed in the training and testing of the classification process. To evaluate the performance of the GSF in EMG signals classification problem, two experiments are considered. The first experiment consists of classification of multiple hand gestures using EMG signals and the second experiment considers classifying phases of hand motion for a grasping task. An array of standard classification techniques are considered in both experiments and the use of GSF in filtering out noise is shown to enhance classification accuracy with remarkably reduced computational time for the considered classification techniques. This illustrates the feasibility of GSF in filtering EMG signals for classification tasks. To gain further insights into the GSF, its performance is compared with that of a median filter (MF), one of the well-known filtering techniques. By using the overall classification accuracy as an index of comparison, the GSF is shown to result in a superior classification accuracy, demonstrating its efficacy for EMG signals filtering process. Thus, employing GSF proves to provide enhancement in the classification accuracy and required computational efforts.

This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequency (MPF), and electromyography power (TP) to represent the surface electromyography (EMG) signal; lastly, the extracted features are input into a one-class support vector machine to determine whether the player has been fatigued and are input into a weighted support vector machine to determine the degree of fatigue if the player has been fatigued. The experimental results show that more than 95% of the fatigue state can be recognized by surface electromyography (EMG) signal.

Automatic analysis of motor unit potentials

An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate (93.9%±2.6%), and error in estimating the number of MUPTs (0.3±0.5) achieved for 10 simulated EMG signals comprised of 3-11 MUPTs are encouraging for using the system for decomposing various EMG signals.

Control of transhumeral prosthetic devices can effectively be performed using the predicted joint torques at the elbow. The joint torque values are generally predicted using the Electromyography (EMG) signals taken from upper arm muscles of the amputee. This paper uses a Bagnoli-16 EMG system to extract EMG signals from the biceps and triceps. The EMG signals are complex to handle mainly due to the stochastic nature of the signal. Independent component analysis (ICA) is utilized to isolate the EMG signals from each muscle. In order to measure the actual torque, a novel kinematic model is proposed in this paper. For the joint torque prediction two classifiers have been developed. First an Artificial Neural Network model (ANN) based classifier is trained to predict the joint torques. Using different test data the ANN model is tested against the arm kinematic based joint torque predictions. The test results indicated 5.6% of root mean square error against the actual predicted torque values. In order to improve the classification an Artificial Neuro-Fuzzy inference system (ANFIS) has been developed. Using the same data the ANFIS based classifier produced 3.3% of the root mean square error against the kinematically predicted joint torques.

Background: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs. Objective:Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG. Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too. Results: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL. Conclusion:Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development and modern Human Computer Interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. For properly analyze EMG signal there need good quality of decomposition so that it can reveal the total characteristics of EMG signals. Because EMG signal is Non-Stationary signal so it needs such a method that can decompose non-stationary signal thus wavelet decomposition is a good choice for this type. There are different types of wavelet available. Henceforth, it is necessary that proper attempt should be taken to choice the best one. Here analyses of EMG Signals were made by Various Wavelet Decomposition method with different types of wavelet and it illustrates the comparative study on best possible energy localization in the time-scale plane in order to show the performance. Thus we can choice the right one. The EMG signals used for this analysis - were found both from locally collected as well as from www.emglab.net[5] which provides EMG signal related raw data and other facilities.

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%).

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).

EMG Signals based Human Action Recognition via Deep Belief Networks

As the development of those Electromyography assisted biodynamic models, the requirements of Electromyography (EMG) signals have become increasingly important. Traditionally, subjects always need whenever experiment need to put into practice. Therefore, the requirements of EMG simulation increased significantly. The traditional method for the simulation of EMG is based on the physiological mechanisms of muscle contraction. So, it is very important to deeply understand the physiological mechanisms of muscle contraction. Actually, this mechanism is very complicated, and it is usually very hard to simulation the specific EMG signals for a specific subject. A new technique was developed. It is based on the experiment data and eliminates the need to collect EMG signals by electrode. This new technique demonstrated at least as similar signal shapes and similar model fidelity when compared to the traditional technique, indicating that it is a valid alternative to traditional method. The subjects who were once joined the experiment and the EMG collection systems are no longer needed. In particular, this technique will be valuable for evaluating some exertion once can't be taken, or can't be finished by the specific subject. In addition, based on those already existing subjects, it is very easy to set the relevant properties of specific subject, and then generate virtual subject and the simulated EMG signal according to any simulation conditions.