Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results

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.

People with disabilities correspond to about 25% of the Brazilian population. A great part of these people have physical impairments that difficult the use computer peripherals. This article presents the development of a system for detection of hand movements through the acquisition and classification of electromyographic (EMG) signals using machine learning techniques. The purpose of the proposed system is to be used by people with disabilities to control an adapted text editor. The signals are capture by surface EMG electrodes and used to the detect 4 different hand movements. In addition, a database with 3200 EMG signals generated by the hand movements was created, made by one user diagnosed with cerebral palsy and another user without diagnosed motor disabilities. Several tests were carried out, showing the good accuracy of the proposed system, with a success classification rate of 96% to 98%.

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.

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.

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.

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.

A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.

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.

Relationships of Vibromyographic and Electromyographic Signals During Isometric Voluntary Contraction

The control of multifunctional myoelectric prostheses is commonly limited due to the lack of sufficient electromyography (EMG) signals after amputation. With a goal of developing easy-to-use and flexibly controlled multifunctional prostheses, the authors propose two control strategies that fuse EMG and speech signals. In the first, speech signals switch the joint-mode, and EMG signals determine a motion class to actuate the target movement. In the second, speech signals select a motion class directly, and EMG signals actuated the movement. Experimental results showed that the proposed strategies could improve control performance and enhance the operational efficiency significantly, suggesting that signal fusion is a feasible way to effectively strengthen interactions between humans and machines.

Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction (HCI) system. In this work, the detection of different hand movements (left, right, up and down) was obtained using artificial neural network (ANN). A back-propagation (BP) network with Levenberg-Marquardt training algorithm was utilized. The conventional time and time-frequency based feature sets have been chosen to train the neural network. The simulation results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%.

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.

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.