Multi-channel Electromyography Signals Research Articles

Are the antagonist muscle fatigued during a prolonged isometric fatiguing elbow flexion at very low forces for young adults?

The aim of this study was to examine whether antagonist muscles may be fatigued during a prolonged isometric fatiguing elbow flexion at very low forces. Twelve healthy male subjects sustained an isometric elbow flexion at 10% maximal voluntary contraction torque until exhaustion while multichannel electromyographic signals were collected from the biceps brachii (BB) and triceps brachii (TB). Muscle fiber conduction velocity (CV) and fractal dimension (FD) of both muscles were calculated to reflect peripheral and central fatigue. CV and FD of TB as well as FD of BB decreased progressively during the sustained fatiguing contraction, while the CV of BB declined at the beginning of the contraction and then increased progressively until the end of the contraction. The result may indicate that during the sustained low-force isometric fatiguing contraction, antagonist muscle may be peripherally fatigued, and changes in coactivation activities were modulated not only by central neuronal mechanisms of common drive but also by peripheral metabolic factors.

An enhanced EMG-driven Musculoskeletal model based on non-negative matrix factorization

In recent years, electromyography (EMG) signals have been widely used in human–machine interfaces (HMIs). Musculoskeletal models (MMs) have been employed to decode human movement intentions from EMG signals in HMIs. Non-negative matrix factorization (NMF) has been adopted to extract neural control information from surface EMG signals. The aim of this study was to investigate if the NMF could extract the control information from surface EMG signals to substitute muscle activations of the deep muscles of the MM. An enhanced MM based on NMF (NMF-MM) was proposed to predict wrist pronation/supination (Pro/Sup) joint angles from surface EMG signals. We extracted the muscle activations of a pair of virtual agonist–antagonistic muscles with NMF from multi-channel EMG signals and inputted the extracted signals into the MM. Eight able-bodied subjects were recruited and tested in four different upper limb postures. Eight bipolar electrodes were attached to the upper forearm to record EMG signals. The Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and predicted joint angles were adopted to quantify the performance of the decoding methods. The performance of the proposed NMF-MM was compared with that of the NMF, linear regression (LR), artificial neural network (ANN), LR based on NMF (NMF-LR), ANN based on NMF (NMF-ANN), LR based on time-domain features (TD-LR), and ANN based on time-domain features (TD-ANN). The results demonstrated that the NMF-MM outperformed the other methods with higher prediction accuracy and better robustness. This study proposed a promising method for predicting continuous and coordinated movements in HMIs.

Spatial distribution of erector spinae activity is related to task-specific pain-related fear during a repetitive object lifting task

Fear-avoidance beliefs, particularly the fear of lifting with a flexed spine, are associated with reduced spinal motion during object lifting. Low back pain patients thereby also showed potentially clinically relevant changes in the spatial distribution of back muscle activity, but it remains unknown whether such associations are also present in pain-free individuals. This cross-sectional observational study investigated the relationship between fear-avoidance beliefs and the spatial distribution of lumbar paraspinal muscle activity in pain-free individuals during a repetitive lifting task. Thirty participants completed two pain-related fear questionnaires and performed 25 repetitions of lifting a 5 kg-box from a lower to an upper shelf and back, while multi-channel electromyographic signals were recorded bilaterally from the lumbar erector spinae muscles. Changes in spatial distribution were defined as the differences in vertical position of the weighted centroids of muscle activity (centroid shift) between the first and last few repetitions. Linear regression analyses were performed to examine the relationships between centroid shift and fear-avoidance belief scores. Fear of lifting an object with a flexed spine was negatively associated with erector spinae activity centroid shift (R2 adj. = 0.1832; p = 0.045), which might be an expression of behavioral alterations to prevent the back from possible harm.

Spatio-temporal warping for myoelectric control: an offline, feasibility study

Objective. The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account. Approach. Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals. Main results. Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5%–17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNNs), STW outperformed by 5%–18%. Also, STW showed enhanced performance when compared to the CNN + LSTM model by 2%–14%. All differences were statistically significant with a large effect size. Significance. This feasibility study provides evidence supporting the hypothesis that the STW feature of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors.

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

Central Tendon Injury Impairs Regional Neuromuscular Activation of the Rectus Femoris Muscle.

We aimed to uncover which rectus femoris strain injury types affect regional activation within the rectus femoris. The rectus femoris has a region-specific functional role; the proximal region of the rectus femoris contributes more than the middle and distal regions during hip flexion. Although a history of strain injury modifies the region-specific functional role within the rectus femoris, it was not obvious which rectus femoris strain injury types affect regional activation within it. We studied 12 soccer players with a history of rectus femoris strain injury. Injury data were obtained from a questionnaire survey and magnetic resonance imaging. To confirm the region-specific functional role of the rectus femoris, surface multichannel electromyographic signals were recorded. Accordingly, eight legs had a history of central tendon injury, four had a history of myofascial junction injury, and four had a healed strain injury. When the injury was limited to the central tendon, the region-specific functional role disappeared. The region-specific functional role was confirmed when the injury was outside the central part. The neuromuscular function was also inhibited when the longitudinal range of the injured region was long. Our findings suggest that a central tendon injury with a long injury length impairs regional neuromuscular activation of the rectus femoris muscle.

Classifying Upper Arm Gym-Workouts via Convolutional Neural Network by Imputing a Biopotential-Kinematic Relationship

This paper proposes a systemic approach to upper arm gym-workout classification according to spatio-temporal features depicted by biopotential as well as joint kinematics. The key idea of the proposed approach is to impute a biopotential-kinematic relationship by merging the joint kinematic data into a multichannel electromyography signal and visualizing the merged biopotential-kinematic data as an image. Under this approach, the biopotential-kinematic relationship can be imputed by counting on the functionality of a convolutional neural network: an automatic feature extractor followed by a classifier. First, while a professional trainer is demonstrating upper arm gym-workouts, electromyography and joint kinematic data are measured by an armband-type surface electromyography (sEMG) sensor and a RGB-d camera, respectively. Next, the measured data are augmented by adopting the amplitude adjusted Fourier Transform. Then, the augmented electromyography and joint kinematic data are visualized as one image by merging and calculating pixel components in three different ways. Lastly, for each visualized image type, upper arm gym-workout classification is performed via the convolutional neural network. To analyze classification accuracy, two-way rANOVA is performed with two factors: the level of data augmentation and visualized image type. The classification result substantiates that a biopotential-kinematic relationship can be successfully imputed by merging joint kinematic data in-between biceps- and triceps-electromyography channels and visualizing as a time-series heatmap image.

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.

The Effect of ICA and Non-negative Matrix Factorization Analysis for EMG Signals Recorded From Multi-Channel EMG Sensors.

Surface electromyography (EMG) measurements are affected by various noises such as power source and movement artifacts and adjacent muscle activities. Hardware solutions have been found that use multi-channel EMG signal to attenuate noise signals related to sensor positions. However, studies addressing the overcoming of crosstalk from EMG and the division of overlaid superficial and deep muscles are scarce. In this study, two signal decompositions—independent component analysis and non-negative matrix factorization—were used to create a low-dimensional input signal that divides noise, surface muscles, and deep muscles and utilizes them for movement classification based on direction. In the case of index finger movement, it was confirmed that the proposed decomposition method improved the classification performance with the least input dimensions. These results suggest a new method to analyze more dexterous movements of the hand by separating superficial and deep muscles in the future using multi-channel EMG signals.

Real-Time Classification of Multi-Channel Forearm EMG to Recognize Hand Movements using Effective Feature Combination and LDA Classifier

Electromyography (EMG) signals acquired from surface of arms can be crucial in recognizing nature of hand gestures. The concept is used in current highly demanding fields such as controlling prosthetic limbs, diagnosing neuromuscular disorders, manipulation of robotic arm etc. The purpose of the work was to classify a set of hand motions from corresponding multi-channel surface EMG signals by developing MATLAB tools. The research focused on extracting multiple signal features and finding the appropriate combination of extracted intelligible features to get the best classification accuracy for the specific set of hand gestures. For dynamic and fast classification purpose, linear discriminant analysis (LDA) classifier was employed. Effect of feature dimensionality reduction on classification accuracy was also investigated via Principal Component Analysis (PCA) in this research. Finally, the research analyzed different electrode placements by comparing classification accuracy for each of the set of motions and proposed a simple and compact data acquisition instrumentation having less number of electrodes while maintaining high classification accuracy.Bangladesh Journal of Medical Physics Vol.10 No.1 2017 25-39

Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application

Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm muscles for the validation of developed hardware. The muscle’s position has been selected by palpation method. Furthermore, the classification algorithms have been examined for seven different activities. Total 29 subjects have been chosen (25 intact and four Amputees) to acquire the EMG data by these activities. To classify the recorded EMG data set, nine time domain and seven frequency domain features have been extracted. A comparative analysis of different classifiers is presented for different muscle position of electrodes. The signal processing and classification algorithms have been processed in MATLAB 2016a. The accuracy of classification ranges for different classification algorithms from 57.69% to 99.92% for all subjects.

A 3-DOF hemi-constrained wrist motion/force detection device for deploying simultaneous myoelectric control.

For describing the state of the wrist, either the force or movement of wrist can be measured as the training target in the simultaneous electromyography control. However, the relationship between the force and movement is so complex that only the force or movement is not precise enough to describe its actual situations. In this paper, we propose a novel platform that can acquire three degrees of freedom (DOF) wrist motion/force synchronously with multi-channel electromyography signals in a hemi-constraint way. The self-made wrist force-movement mapping device establishes a stable relationship between the wrist movement and force. Meanwhile, the elicited wrist movement can be directly fed back to the subjects via laser cursor. The information of the cursor can directly reflect the 3-DOF movement of the wrist without any decoupling algorithms. Through this platform, the support vector regression model learned from the training data can well predict the arbitrary combinations of 3-DOF wrist movements. The cross-validation result indicates that the regression accuracy of free 3-DOF movements can reach a similar performance to that of 2-DOF regular movements (in terms of R2, regular movement vs. free movement, p > 0.1). Graphical abstract The hemi-constrained platform used for detecting 3-DOF wrist movements.

Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks

Abstract Surface electromyography (EMG) signals have been widely used in locomotion studies and human-machine interface applications. In this paper, a regression model which relates the multichannel surface EMG signals to human lower limb flexion/extension (FE) joint angles is constructed. In the experimental paradigm, three dimensional trajectories of 16 external markers on the human lower limbs were recorded by optical motion capture system and surface EMG signals from 10 muscles directly concerned with the lower limb motion were recorded synchronously. With the raw data, the joint angles of hip, knee and ankle were calculated accurately and the time series of intensity for surface EMG signals were extracted. Then, a deep belief networks (DBN) that consists of restricted Boltzmann machines (RBM) was built, by which the multi-channel processed surface EMG signals were encoded in low dimensional space and the optimal features were extracted. Finally, a back propagation (BP) neural network was used to map the optimal surface EMG features to the FE joint angles. The results show that, the features extracted from multichannel surface EMG signals using DBN method proposed in this paper outperform principal components analysis (PCA), and the root mean square error (RMSE) between the estimated joint angles and calculated ones during human walking is reduced by about 50%. The proposed model is expected to develop human-machine interaction interface to achieve continuous bioelectric control and to improve motion stability between human and machine, especially for lower limb wearable intelligent equipment.

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors.

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

BackgroundEnsemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.ExperimentThe experimental data was obtained from the Center for Machine Learning and Intelligent Systems, University of California Irvine (UCI). The data was donated by the Nueva Granada Military University and the Technopark node Manizales in Colombia. The databases of 11 male subjects from the healthy group were taken into the study. The subjects undergo three exercise programs, leg extension from a sitting position (sitting), flexion of the leg up (standing), and gait (walking), while four electrodes were placed on biceps femoris (BF), vastus medialis (VM), rectus femoris (RF), and semitendinosus (ST).MethodsBased on the experimental data, a comparative study is provided by assessing the Empirical Mode Decomposition (EMD)-based approaches, EEMD, Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). The outcomes from these approaches are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing.ResultsBoth MEMD and NA-MEMD methods (except EEMD) can guarantee equal numbers of IMFs. For mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD.ConclusionsThis study proposes the NA-MEMD approach for multichannel EMG signal processing. This finding implies that NA-MEMD is effective for simultaneously analysing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing the functional activities of daily living.

Performance evaluation of Noise-Assisted Multivariate Empirical Mode Decomposition and its application to multichannel EMG signals.

The use of the Empirical Mode Decomposition (EMD) for nonlinear signal processing has been popularized in the recent years. However, its utility for the processing of multichannel Electromyography (EMG) signals is still limited. This paper investigates the decomposition performance of multichannel EMGs by using the EMD-based approaches, Ensemble EMD (EEMD), Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). In the experiment, 11 male subjects undergo three exercise programs, leg extension from a sitting position, flexion of the leg up, and gait, while electrodes are placed on the muscle groups, biceps femoris, vastus medialis, rectus femoris, and semitendinosus. The outcomes are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing. Results show both MEMD and NA-MEMD can guarantee equal numbers of IMFs, whereas for mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD. This finding implies that NA-MEMD is effective for simultaneously analyzing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing functional activities of daily living.

Muscle fiber conduction velocity and fractal dimension of EMG during fatiguing contraction of young and elderly active men

Over the past decade, linear and nonlinear surface electromyography (EMG) variables highlighting different components of fatigue have been developed. In this study, we tested fractal dimension (FD) and conduction velocity (CV) rate of changes as descriptors, respectively, of motor unit synchronization and peripheral manifestations of fatigue. Sixteen elderly (69 ± 4 years) and seventeen young (23 ± 2 years) physically active men (almost 3–5 h of physical activity per week) executed one knee extensor contraction at 70% of a maximal voluntary contraction for 30 s. Muscle fiber CV and FD were calculated from the multichannel surface EMG signal recorded from the vastus lateralis and medialis muscles.The main findings were that the two groups showed a similar rate of change of CV, whereas FD rate of change was higher in the young than in the elderly group. The trends were the same for both muscles.CV findings highlighted a non-different extent of peripheral manifestations of fatigue between groups. Nevertheless, FD rate of change was found to be steeper in the elderly than in the young, suggesting a greater increase in motor unit synchronization with ageing. These findings suggest that FD analysis could be used as a complementary variable providing further information on central mechanisms with respect to CV in fatiguing contractions.

Electromyographic adjustments during continuous and intermittent incremental fatiguing cycling.

We studied the sensitivity of electromyographic (EMG) variables to load and muscle fatigue during continuous and intermittent incremental cycling. Fifteen men attended three laboratory sessions. Visit 1: lactate threshold, peak power output, and VO2max . Visits 2 and 3: Continuous (more fatiguing) and intermittent (less fatiguing) incremental cycling protocols [20%, 40%, 60%, 80% and 100% of peak power output (PPO)]. During both protocols, multichannel EMG signals were recorded from vastus lateralis: muscle fiber conduction velocity (MFCV), instantaneous mean frequency (iMNF), and absolute and normalized root mean square (RMS) were analyzed. MFCV differed between protocols (P < 0.001), and only increased consistently with power output during intermittent cycling. RMS parameters were similar between protocols, and increased linearly with power output. However, only normalized RMS was higher during the more fatiguing 100% PPO stage of the continuous protocol [continuous-intermittent mean difference (95% CI): 45.1 (8.5% to 81.7%)]. On the contrary, iMNF was insensitive to load changes and muscle fatigue (P = 0.14). Despite similar power outputs, continuous and intermittent cycling influenced MFCV and normalized RMS differently. Only normalized RMS was sensitive to both increases in power output (in both protocols) and muscle fatigue, and thus is the most suitable EMG parameter to monitor changes in muscle activation during cycling.

A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition

Electromyography (EMG) is widely applied for neural engineering. For motion pattern recognition, many features of multi-channel EMG signals were investigated, but the relationships between muscles were not considered. In this study, a novel STFT-ranking feature based on short-time Fourier transform (STFT) is proposed. The novelty of STFT-ranking features is considering and covering the relationship information between EMG signals and multiple muscles in a motion pattern. With an exoskeleton robot arm, two series of motion patterns corresponding to the shoulder and elbow in the sagittal plane were investigated. EMG signals from six muscles were acquired in arm motion patterns when participants worn the robot arm. Four types of feature combinations, including seven conventional features, were compared with the STFT-ranking feature. The principal component analysis (PCA) and support vector machine (SVM) were used to build the motion recognition model. With the STFT-ranking feature, the recognition performance (93.9%) is superior to the conventional features (33.3–90.8%). The recognition variation is smaller (SD=4.3%) than the other features tested (SD=5.9–13.8%). These achievements will contribute to the advancement of control method of exoskeleton robots or power orthoses based on multi-channel EMG signals in the future. Based on the principle of STFT-ranking feature, the method also has potential for other multi-channel signal applications, such as electroencephalography (EEG) signal processing, speech recognition, and acoustic analysis.

Continuous motion decoding from EMG using independent component analysis and adaptive model training.

Surface Electromyography (EMG) is popularly used to decode human motion intention for robot movement control. Traditional motion decoding method uses pattern recognition to provide binary control command which can only move the robot as predefined limited patterns. In this work, we proposed a motion decoding method which can accurately estimate 3-dimensional (3-D) continuous upper limb motion only from multi-channel EMG signals. In order to prevent the muscle activities from motion artifacts and muscle crosstalk which especially obviously exist in upper limb motion, the independent component analysis (ICA) was applied to extract the independent source EMG signals. The motion data was also transferred from 4-manifold to 2-manifold by the principle component analysis (PCA). A hidden Markov model (HMM) was proposed to decode the motion from the EMG signals after the model trained by an adaptive model identification process. Experimental data were used to train the decoding model and validate the motion decoding performance. By comparing the decoded motion with the measured motion, it is found that the proposed motion decoding strategy was feasible to decode 3-D continuous motion from EMG signals.