Alterations of Motor Unit Characteristics Associated With Muscle Fatigue

This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before/after, and during muscle fatigue were identified for analysis. The surface location of the innervation zones (IZs) of the MUs was identified from the HD-sEMG bipolar motor unit action potential (MUAP) map. The depth of the MU was also identified from the decay pattern of the MUAP along the muscle fiber transverse direction. Both the surface IZ location and the MU depth information were utilized to ensure the same MU was examined during the contraction before/after muscle fatigue. The MUAP similarity, defined as the correlation coefficient between MUAP morphology, was adopted to reveal the alterations in MU characteristics under the condition of fatigue. The biomarkers of the same MUs were compared before/after fatigue (task 1) at 5%, 10%, and 15% maximal voluntary contraction (MVC) and in the process of continuous fatigue (task 2) at 20% MVC. Our results indicate that the MUAP morphology similarity of the same MUs was 0.91 ± 0.06 (task 1) and 0.93 ± 0.04 (task 2). The results showed that MUAP morphology maintained good stability before/after, and during muscle fatigue. The findings of this study may advance our understanding of the mechanism of MU neuromuscular fatigue.

Spatiotemporal characteristics of lower back muscle fatigue during a ten minutes endurance test at 50% upper body weight in healthy inactive, endurance, and strength trained subjects

In modern developed societies, heavy physical demands are decreasing and getting replaced by longer periods of static, low-exertion activities such as sitting or standing. To counteract this lack of physical activity, more and more people are engaging in physical activity through exercise and training. Virtually opposite training modalities are endurance and strength. We asked if back muscle endurance capacity is influenced by training mode. 38 healthy male subjects (age range 19–31 years, mean age 22.6 years) were investigated: sedentary (Control, n = 12), endurance trained (ET, n = 13), and strength trained participants (ST, n = 13). They underwent a ten-minutes isometric extension task at 50% of their upper body weight. Surface EMG was measured in the low-back region utilizing quadratic 4*4 monopolar electrode montages per side. Relative amplitude and mean frequency changes were analysed with respect to electrode position and group during the endurance task. Eight ST subjects failed to complete the endurance task. Relative amplitude and frequency changes were largest in the ST group, followed by Control and ET groups (amplitude: F 6.389, p 0.004, frequency: F 11.741, p<0.001). Further, independent of group largest amplitude increase was observed for the most upper and laterally positioned electrodes. Mean frequency changes showed no systematic spatial distribution pattern. Although, in the light of an aging population, strength training has its merits our results question the functional suitability of frequent and isolated high-impact strength training for everyday endurance requirements like doing the dishes. Fatigue related amplitude elevations are systematically distributed in the back region, showing least fatigue signs for the most caudal and medial, i.e. the lumbar paravertebral region.

ANALYSIS OF INDUCED ISOMETRIC FATIGUING CONTRACTIONS IN BICEPS BRACHII MUSCLES USING MYOTONOMETRY AND SURFACE ELECTROMYOGRAPHIC MEASUREMENTS

Viscoelastic properties of skeletal muscle tissue are known to be impacted by fatiguing contractions. In this study, an attempt has been made to utilize myotonometry for analyzing the relationship between muscle viscoelasticity and contractile behaviors in a fatiguing task. For this purpose, thirteen young healthy volunteers are recruited to perform the fatiguing isometric task and the time to task failure (TTF) is recorded. Myotonometric parameters and simultaneous surface electromyographic (sEMG) signals are recorded from the Biceps Brachii muscle of the flexed arm. The correlation between myotonometric parameters and TTF is further analyzed. Cross-validation with sEMG features is also performed. Stiffness of muscle has a positive correlation with TTF in the left hand ([Formula: see text]). Damping property of the nonfatigued muscle is positively associated with the fatigue-induced changes in amplitude features of sEMG signal in the right hand ([Formula: see text]). The normalized rate of change of mean frequency of sEMG signal has a positive correlation with stiffness values in both of the hands ([Formula: see text]). Muscle viscoelasticity is demonstrated to influence the progression of fatigue, although the difference in motor control due to handedness is also found to be an important factor. The results are promising to improve the understanding of the effect of muscle mechanics in fatigue-induced task failure.

Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals

In this study, an attempt has been made to develop an automated muscle fatigue detection system using cyclostationary based geometric features of surface electromyography (sEMG) signals. For this purpose, signals are acquired from fifty-eight healthy volunteers under dynamic muscle fatiguing contractions. The sEMG signals are preprocessed and the epochs of signals under nonfatigue and fatigue conditions are considered for the analysis. A computationally effective Fast Fourier transform based accumulation algorithm is adapted to compute the spectral correlation density coefficients. The boundary of spectral density coefficients in the complex plane is obtained using alpha shape method. The geometric features, namely, perimeter, area, circularity, bending energy, eccentricity and inertia are extracted from the shape and the machine learning models based on multilayer perceptron (MLP) and extreme learning machine (ELM) are developed using these biomarkers. The results show that the cyclostationarity increases in fatigue condition. All the extracted features are found to have significant difference in the two conditions. It is found that the ELM model based on prominent features classifies the sEMG signals with a maximum accuracy of 94.09% and F-score of 93.75%. Therefore, the proposed approach appears to be useful for analysing the fatiguing contractions in neuromuscular conditions.

Quantitative Analysis of EEG Power Spectrum and EMG Median Power Frequency Changes after Continuous Passive Motion Mirror Therapy System

Robotic mirror therapy (MT), which allows movement of the affected limb, is proposed as a more effective method than conventional MT (CMT). To improve the rehabilitation effectiveness of post-stroke patients, we developed a sensory stimulation-based continuous passive motion (CPM)-MT system with two different operating protocols, that is, asynchronous and synchronous modes. To evaluate their effectiveness, we measured brain activation through relative and absolute power spectral density (PSD) changes of electroencephalogram (EEG) mu rhythm in three cases with CMT and CPM-MT with asynchronous and synchronous modes. We also monitored changes in muscle fatigue, which is one of the negative effects of the CPM device, based on median power frequency (MPF) and root mean square (RMS). Relative PSD was most suppressed when subjects used the CPM-MT system under synchronous control: 22.11%, 15.31%, and 16.48% on Cz, C3, and C4, respectively. The absolute average changes in MPF and RMS were 1.59% and 9.78%, respectively, with CPM-MT. Synchronous mode CPM-MT is the most effective method for brain activation, and muscle fatigue caused by the CPM-MT system was negligible. This study suggests the more effective combination rehabilitation system for MT by utilizing CPM and magnetic-based MT task to add action execution and sensory stimulation compared with CMT.

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN

This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG signal. This work presents a global EMG index map by using the new analytical technique based on the relationship between the average IEMG and mean power frequency (MPF) values. The proposed method enables simultaneous estimation of muscle fatigue level and force using real-time EMG signals from the femoral and gastrocnemius muscles.

A NOVEL APPROACH FOR THE PATTERN RECOGNITION OF HAND MOVEMENTS BASED ON EMG AND VPMCD

Essentially, the classification of human hand movements is a process of pattern recognition. However, existing computationally intense and complex pattern recognition methods have failed thus far to be optimally successful in constructing associations between extracted signal features. Due to such limitations, a new pattern recognition method using variable predictive model-based class discrimination (VPMCD) is proposed. This approach considers that the feature values can exhibit inter-relations in nature and such associations will show different forms in different classes. In practice, this is always true for different hand movements. The signals produced by electromyography (EMG) and received from human arm muscles, are characteristically non-linear and non-stationary. A novel hand gesture recognition technique, based on wavelet feature extraction and VPMCD is proposed. First, the maximum values of the wavelet coefficient are extracted as the feature vectors from the surface EMG signals after de-noising. Then, the feature values are regarded as the inputs of the VPMCD classifier. Finally, four movement patterns (hand clenching, hand extension, wrist flexion, and wrist extension) are identified by the outputs of the VPMCD classifier. Our analysis results show that the proposed pattern recognition approach can distinguish different gestures successfully and effectively. Simultaneously, compared with the artificial neural network and the support vector machine classifier, more accurate recognition can be achieved using our proposed technique.

Measuring the interactions between different locations in a muscle to monitor localized muscle fatigue

In this study we investigated a technique for estimating the progression of localized muscle fatigue. This technique measures the dependence between motor units using high density surface electromyogram (HD-sEMG) and is based on the Normalized Mutual Information (NMI) measure. The NMI between every pair combination of the electrode array is computed to measure the interactions between electrodes. Participants in the experiment had an array of 64 electrodes (16 by 4) placed over the TA of their dominate leg such that the columns of the array ran parallel with the muscle fibers. The HD-sEMG was recorded whilst the participants maintained an isometric dorsiflexion with their dominate foot until task failure at 40% and 80% of their maximum voluntary contraction (MVC). The interactions between different locations over the muscle were computed using the recorded HD-sEMG signals. The results show that the average interactions between various locations over the TA significantly increased during fatigue at both levels of contraction. This can be attributed to the dependence in the motor units.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

Muscle Fatigue Analysis of the Deltoid during Three Head-Related Static Isometric Contraction Tasks

This study aimed to investigate the fatiguing characteristics of muscle-tendon units (MTUs) within skeletal muscles during static isometric contraction tasks. The deltoid was selected as the target muscle and three head-related static isometric contraction tasks were designed to activate three heads of the deltoid in different modes. Nine male subjects participated in this study. Surface electromyography (SEMG) signals were collected synchronously from the three heads of the deltoid. The performances of five SEMG parameters, including root mean square (RMS), mean power frequency (MPF), the first coefficient of autoregressive model (ARC1), sample entropy (SE) and Higuchi’s fractal dimension (HFD), in quantification of fatigue, were evaluated in terms of sensitivity to variability ratio (SVR) and consistency firstly. Then, the HFD parameter was selected as the fatigue index for further muscle fatigue analysis. The experimental results demonstrated that the three deltoid heads presented different activation modes during three head-related fatiguing contractions. The fatiguing characteristics of the three heads were found to be task-dependent, and the heads kept in a relatively high activation level were more prone to fatigue. In addition, the differences in fatiguing rate between heads increased with the increase in load. The findings of this study can be helpful in better understanding the underlying neuromuscular control strategies of the central nervous system (CNS). Based on the results of this study, the CNS was thought to control the contraction of the deltoid by taking the three heads as functional units, but a certain synergy among heads might also exist to accomplish a contraction task.

Estimating the progression of muscle fatigue based on dependence between motor units using high density surface electromyogram

In this study we have tested the hypothesis regarding the increase in synchronization with the onset of muscle fatigue. For this aim, we have investigated the difference in the synchronicity between high density surface electromyogram (sEMG) channels of the rested muscles and when at the limit of endurance. Synchronization was measured by computing and normalizing the mutual information between the sEMG signals recorded from the high-density array electrode locations. Ten volunteers (Age range: 21 and 35 years; Mean age = 26 years; Male = 6, Female = 4) participated in our experiment. The participants performed isometric dorsiflexion of their dominate foot at two levels of contraction; 40% and 80% of their maximum voluntary contraction (MVC) until task failure. During the experiment an array of 64 electrodes (16 by 4) placed over the TA parallel to the muscle fiber was used to record the HD-sEMG. Normalized Mutual Information (NMI) between electrodes was calculated using the HD-sEMG data and then analyzed. The results show that that the average NMI of the TA significantly increased during fatigue at both levels of contraction. There was a statistically significant difference between NMI of the rested muscle compared with it being at the point of task failure.

Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study

The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters.

Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition

We propose a joint force estimation method to compute elbow flexion force using surface electromyogram (sEMG) considering time-varying effects in a fatigue condition. Muscle fatigue is a major cause inducing sEMG changes with respect to time over long periods and repetitive contractions. The proposed method composed the muscle-twitch model representing the force generated by a single spike and the spikes extracted from sEMG. In this study, isometric contractions at six different joint angles (10 subjects) and dynamic contractions with constant velocity (six subjects) were performed under non-fatigue and fatigue conditions. Performance of the proposed method was evaluated and compared with that of previous methods using mean absolute value (MAV). The proposed method achieved average 6.7 ± 2.8 %RMSE for isometric contraction and 15.6 ± 24.7%RMSE for isokinetic contraction under fatigue condition with more accurate results than the previous methods.

Inter-Gender sEMG Evaluation of Central and Peripheral Fatigue in Biceps Brachii of Young Healthy Subjects

Purpose

The purpose of the present study was to evaluate inter-arm and inter-gender differences in fractal dimension (FD) and conduction velocity (CV) obtained from multichannel surface electromyographic (sEMG) recordings during sustained fatiguing contractions of the biceps brachii.

Methods

A total of 20 recreationally active males (24±6 years) and 18 recreationally active females (22±9 years) performed two isometric contractions at 120 degrees elbow joint angle: (1) at 20% maximal voluntary contraction (MVC) for 90 s, and (2) at 60% MVC until exhaustion the time to perform the task has been measured. Signals from sEMG were detected from the biceps brachii using bidimensional arrays of 64 electrodes and initial values and rate of change of CV and FD of the sEMG signal were calculated.

Results

No difference between left and right sides and no statistically significant interaction effect of sides with gender were found for all parameters measured. A significant inter-gender difference was found for MVC (p<0.0001). Initial values of CV were higher in females than in males at both force levels (20% MCV: p<0.0001; 60% MCV: p<0.05) whereas a lower initial estimate of FD was observed in females compared to males (20% MCV: p<0.05; 60% MCV: p<0.0001). No difference in CV and FD slopes was found at 20% MVC between genders. At 60% MVC significantly lower CV and FD slopes (CV and FD: p<0.05) and a more protracted time to exhaustion were found in females than in males (p<0.0001). When considering time to exhaustion at both levels of contraction no difference in percentage change (Δ%) of CV and FD slopes was found between genders (p>0.05). During the sustained 60% MVC no statistical correlation was found between MVC and CV or FD initial estimates nor between MVC and CV or FD slopes both in males and females whereas. A significant positive correlation between CV and FD slopes was found in both genders (males: r = 0,61; females: r = 0,55).

Conclusions

Fatigue determines changes in FD and CV values in biceps brachii during sustained contractions at 60% MVC. In particular males show greater increase in the rate of change of CV and FD than females whereas no difference in percentage change of these sEMG descriptors of fatigue was found. A significant correlation between FD and CV slopes found in both genders highlights that central and peripheral myoelectric components of fatigue may interact during submaximal isometric contractions.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Muscle fatigue estimation with twitch force derived from sEMG peaks

We propose a new method - twitch force - for estimation of the muscle behavior during voluntary contraction for assessing localized muscle fatigue. The proposed method uses the sEMG peaks as input and the measured force as output. The twitch force, which is a transfer function to generate force, was estimated during fatiguing contraction. We verified the estimated twitch force based on the measured results with electrical stimulation. The participants performed isometric little finger flexion until exhaustion. SEMG was recorded on the flexor digiti minimi brevis muscle for the proposed method and the electrical stimulation electrodes on the ulnar nerve induced involuntary contraction for reference. As the muscle fatigue level increased, the twitch peaks decreased in both methods. The proposed method can be widely used in the quantitative analysis of muscle fatigue during voluntary contraction.

Computation and evaluation of features of surface electromyogram to identify the force of muscle contraction and muscle fatigue

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05).

Computation and study of the low-frequency oscillation of surface electromyogram recorded in biceps during isometric upper limb contraction

This study has experimentally studied the low frequency oscillation in surface electromyogram (sEMG) during isometric muscle contraction for Biceps brachii muscle. The time constant corresponding to this low frequency oscillation was computed for sEMG. Experiments were repeated for 25 subjects, and for isometric muscle contraction, ranging between 25% and 100 % maximum voluntary contraction (MVC), while the subjects were given real-time visual feedback of the force of contraction, recorded at 1000 samples/ second. The time constant (Tc) corresponding to the variability of sEMG was computed using the Hilbert transform and envelope detection. The results show that the time constant, Tc of sEMG recorded from the biceps during isometric contraction was the same for all the subjects, and for different levels of force of muscle contraction, and was 78 ms (± 1.1). This suggests that the low frequency oscillation of sEMG of the biceps brachii muscles is a fundamental property of that muscle, and corresponds to a fundamental phenomenon, which has never been observed earlier. By comparison from delays reported in literature, this delay is similar to M2 stretch reflex latency, and may be attributed to the neural pathway delay.

Age-associated changes in muscle activity during isometric contraction

INTRODUCTION

We investigated the effect of age on the complexity of muscle activity and the variance in the force of isometric contraction.

METHODS

Surface electromyography (sEMG) from biceps brachii muscle and force of contraction were recorded from 96 subjects (20-70 years of age) during isometric contractions.

RESULTS

There was a reduction in the complexity of sEMG associated with aging. The relationship of age and complexity was approximated using a bilinear fit, with the average knee point at 45 years. There was an age-associated increase in the coefficient of variation (CoV) of the force of muscle contraction, and this increase was correlated with the decrease in complexity of sEMG (r(2) = 0.76).

CONCLUSIONS

There was an age-associated increase in CoV and also a reduction in the complexity of sEMG. The correlation between these 2 factors can be explained based on the age-associated increase in motor unit density.