Progression Of Muscle Fatigue Research Articles

EVALUATION OF THE EFFECTIVENESS OF AN UNLOADING ORTHOSIS OF THE KNEE JOINT BASED ON INDIRECT INDICATORS AND SURFACE ELECTROMYOGRAPHY DATA

One of the urgent tasks in the process of creating biomechanical devices, namely unloading orthoses, is to determine their effectiveness. The paper presents the results of a study of muscle fatigue of the hip synergists during isotonic contractions when performing squats both with and without the use of a knee joint unloading orthosis. In addition to determining indirect indicators that determine peripheral muscle fatigue, namely the number of squats performed and the speed of their implementation, another effective method for studying the progression of muscle fatigue, the method of surface electromyography, was used in the work. The data obtained in field experiments for both indirect indicators and surface electromyography recordings were processed by calculating the root mean square value, which made it possible to evaluate the effectiveness of the knee joint unloading orthosis using a regression statistical model. The coefficient of absolute slope of the linear regression, built on the basis of experimental data, was chosen as the criterion for evaluating the effectiveness. Surface electromyography data were recorded using the iiitech electromyographic reading module and processed using the Spike Recorder software package supplied with the module. Statistical processing of research results and calculations of linear regression coefficients were carried out using the MS Excel package. The analysis of the results obtained indicates the effectiveness of the use of the orthosis involved in the experiments, the use of which makes it possible to delay the decrease in the production strength of active muscle fibers and leads to a slower onset of peripheral muscle fatigue.

Model-based Fusion of Surface Electromyography with Kinematic and Kinetic Measurements for Monitoring of Muscle Fatigue

This study proposes a novel method for monitoring muscle fatigue using muscle-specific dynamic models which relate joint time-frequency signatures extracted from the relevant electromyogram (EMG) signals with the corresponding estimated muscle forces. Muscle forces were estimated using physics-driven musculoskeletal models which incorporate muscle lengths and contraction velocities estimated from the available kinematic and kinetic measurements. For any specific individual, such a muscle-specific dynamic model is trained using EMG and movement data collected in the early stages of an exercise, i.e., during the least-fatigued behavior. As the exercise or physical activity of that individual progresses and fatigue develops, residuals yielded by that model when approximating the newly arrived data shift and change because of the fatigue-induced changes in the underlying dynamics. In this paper, we propose quantitative evaluation of those changes via the concept of a muscle-specific Freshness Index (FI) which at any given time expresses overlaps between the distribution of that muscle’s model residuals observed on the most recently collected data and the distribution of modeling residuals observed during non-fatigued behavior. The newly proposed method was evaluated using data collected during a repetitive sawing motion experiment with 12 healthy participants. The performance of the FI as a fatigue metric was compared with the performance of the instantaneous frequency of the relevant EMG signals, which is a more traditional and widely used metric of muscle fatigue. It was found that the FI reflected the progression of muscle fatigue with desirable properties of stronger monotonic trends and smaller noise levels compared to the traditional, instantaneous frequency-based metrics.

Characterization of surface electromyography signals of biceps brachii muscle in fatigue using symbolic motif features.

Exercise-induced muscle damage is a condition which results in the loss of muscle function due to overexertion. Muscle fatigue is a precursor of this phenomenon. The characterization of muscle fatigue plays a crucial role in preventing muscle damage. In this work, an attempt is made to develop signal processing methods to understand the dynamics of the muscle's electrical properties. Surface electromyography signals are recorded from 50 healthy adult volunteers under dynamic curl exercise. The signals are preprocessed, and the first difference signal is computed. Furthermore, ascending and descending slopes are used to generate a binary sequence. The binary sequence of various motif lengths is analyzed using features such as the average symbolic occurrence, modified Shannon entropy, chi-square value, time irreversibility, maximum probability of pattern and forbidden pattern ratio. The progression of muscle fatigue is assessed using trend analysis techniques. The motif length is optimized to maximize the rho value of features. In addition, the first and the last zones of the signal are compared with standard statistical tests. The results indicate that the recorded signals differ in both frequency and amplitude in both inter- and intra-subjects along the period of the experiment. The binary sequence generated has information related to the complexity of the signal. The presence of more repetitive patterns across the motif lengths in the case of fatigue indicates that the signal has lower complexity. In most cases, larger motif length resulted in better rho values. In a comparison of the first and the last zones, most of the extracted features are statistically significant with p < 0.05. It is observed that at the motif length of 13 all the extracted features are significant. This analysis method can be extended to diagnose other neuromuscular conditions.

Muscle Fatigue Analysis by Using a Scale Mixture-based Stochastic Model of Surface EMG Signals.

This paper presents the estimation and analysis of surface electromyogram (EMG) signals during fatiguing contractions based on a stochastic EMG model. In the model, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances, facilitating the representation of the variance distribution of EMG signals. The paper proposes a continuous estimation method for variance distribution parameters using a sliding window, enabling the evaluation of the time-varying stochastic properties of EMG signals. Estimation experiments were conducted on six healthy young adults to analyze changes in EMG variance distribution with the progression of muscle fatigue. The results reveal the linear and nonlinear relationships between muscle fatigue and variance distribution parameters.

Subacromial impingement syndrome: An electromyographic study of shoulder girdle muscle fatigue

Muscle fatigue affecting glenohumeral and/or scapular muscles is suggested as one of the contributing factors to the development of subacromial impingement syndrome (SAIS). Nonetheless, the fatigability of shoulder girdle muscles in association with the pathomechanics of SAIS has not been reported. This study aimed to measure and compare fatigue progression within the shoulder girdle musculature of patients and healthy controls. 75 participants including 39 patients (20 females; 19 males) and 36 healthy controls (15 females; 21 males) participated in the study. Study evaluated the progression of muscle fatigue in 15 shoulder girdle muscles by means of surface and fine-wire EMG during submaximal contraction of four distinct movements (abduction, flexion, internal and external rotation). Shoulder strength, subjective pain experience (McGill Pain Questionnaire), and psychological status (Hospital Anxiety and Depression Scale) were also assessed. The results were compared between patient and control groups according to the gender. Despite marked fatigue observed in the majority of muscles particularly during flexion and abduction at 90°, overall results indicated a lower tendency of fatigue progression in the impingement group across the tests (p < 0.05 – p < 0.001). Shoulder Strength, pain experience, and psychological status were significantly different between the two groups (P < .05). Lower tendency to fatigue progression in the impingement group can be attributed to the presence of fear avoidance and pain-related muscle inhibition, which in turn lead to adaptations in motor programme to reduce muscle recruitment and activation. The significantly higher levels of pain experience and anxiety/depression in the impingement group further support this proposition.

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.

Assessment of Muscle Fatigue during Prolonged, Moderate-Intensity Cycling Across Two Age Groups

PURPOSE: To examine the progression of muscle fatigue in lower limb muscles during a prolonged, moderate-intensity cycling protocol and compare adults of two age groups. METHODS: Eighteen male, healthy, novice cyclists, with no history of joint problems, participated in this study and were divided into two groups of young (Y; n=12; age= 22±2 yrs.) and old (O; n=6; age=53±3 yrs.). All participants signed an informed consent, prior to the beginning of the study. After a 2-min warm up, participants engaged in continuous cycling for 30 minutes at a constant workload of 50 W and a cadence of 60 rpm. Cycling period was followed by a 5-min cool-down. During cycling, ten 30-sec epochs of EMG data were collected at 3-min intervals from rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius medialis (GT) of the right leg. Kinematics of the pedal were recorded and synchronized with the EMG data. Median frequency (MDF) of the power spectrum was utilized as an indicator of muscle fatigue. Linear regression was performed on all MDF values of each participant. T-tests were used to compare the regression slopes between the two groups and to determine the presence of sustained muscle fatigue. An ANOVA with repeated measures was used to compare the progression of transient fatigue between the two groups across all epochs. RESULTS: Occurrence of sustained muscle fatigue was not statistically significant (p>0.05) in any of the muscles. The young group showed trends of sustained fatigue by exhibiting negative average slopes for the RF (-0.14±0.77), BF (-0.14±0.78), and GT (-0.28±1.16) muscles. Whereas the older group only showed a negative average slope for the RF muscle (-0.18±0.71). Average MDF of both groups exhibited non-monotonic changes across epochs in all muscles. The transient fatigue and recovery periods appeared more frequently in older adults, compared to young; however, no significant difference (p>0.05) was observed between the two groups in any of the muscles. CONCLUSIONS: None of the groups exhibited significant emergence of transient or sustained muscle fatigue throughout the cycling session. The employed prolonged, moderate-intensity cycling exercise satisfies the ACSM guidelines for maintaining a daily dose of exercise, making it an ideal physical activity alternative for older populations.

Muscle fatigue analysis using surface EMG signals and time–frequency based medium‐to‐low band power ratio

An attempt has been made to analyse the progression of muscle fatigue using mean power ratios (MPRs) derived from B-distribution time–frequency spectrum of surface electromyography signals. For this purpose, signals are recorded from the biceps brachii muscle of 30 healthy adult volunteers. Fifteen subjects carried out isometric contractions and the rest performed dynamic muscle contractions until task failure. The signals are preprocessed and subjected to B-distribution-based time–frequency analysis. Subsequently, the MPR of medium-to-low-frequency band (M/LFB) and high-to-LFB (H/LFB) are extracted from the time–frequency spectrum. Furthermore, the slope is calculated using the linear regression technique. Results show that MPR of M/LFB and H/LFB exhibit a decreasing trend during the progression of muscle fatigue condition. The strength of the peaks present in MPR of M/LFB progressively reduces and approaches zero at task failure for all subjects. Additionally, higher slope values are found in MPR of M/LFB in comparison with H/LFB. It appears that MPR of M/LFB is more sensitive for muscle fatigue analysis and may be used as an index for real-time monitoring in work place.

Surface electromyography based muscle fatigue progression analysis using modified B distribution time–frequency features

Abstract In this work, an attempt has been made to analyze the progression of muscle fatigue using surface electromyography (sEMG) signals and modified B distribution (MBD) based time–frequency analysis. For this purpose, signals are recorded from biceps brachii muscles of fifty healthy adult volunteers during dynamic contractions. The recorded signals are preprocessed and then subjected to MBD based time–frequency distribution (TFD). The instantaneous median frequency (IMDF) is extracted from the time–frequency matrix for different values of kernel parameter. The linear regression technique is used to model the temporal variations of IMDF. Correlation coefficient is computed in order to select the appropriate value for kernel parameter of MBD based TFD. Further, extended version of frequency domain features namely instantaneous spectral ratio (InstSPR) at low frequency band (LFB), medium frequency band (MFB) and high frequency band (HFB) are extracted from the time–frequency spectrum. In addition to these features, IMDF and instantaneous mean frequency (IMNF) are also calculated. The least square error based linear regression technique is used to track the slope variations of these features. The results show that MBD based time–frequency spectrum is able to provide the instantaneous variations of frequency components associated with fatiguing contractions. The values of InstSPR at MFB and HFB regions, IMDF and IMNF show a decreasing trend during the progression of muscle fatigue. However, an increasing trend is observed in LFB regions. Further the coefficient of variation is calculated for all the features. It is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variability across different subjects in comparison with other two features. It appears that this method could be useful in analyzing various neuromuscular activities in normal and abnormal conditions.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

The progression of muscle fatigue during exercise estimation with the aid of high-frequency component parameters derived from ensemble empirical mode decomposition.

Muscle fatigue is often monitored via the median frequency derived from the surface electromyography (sEMG) power spectrum during isometric contractions. The power spectrum of sEMG shifting toward lower frequencies can be used to quantify the electromanifestation of muscle fatigue. The dynamic sEMG belongs to a nonstationary signal, which will be affected by the electrode moving, the shift of the muscle, and the change of innervation zone. The goal of this study is to find a more sensitive and stable method in order to sense the progression of muscle fatigue in the local muscle during exercise in healthy people. Five male and five female volunteers participated. Each subject was asked to run on a multifunctional pedaled elliptical trainer for about 30 min, twice a week, and was recorded a total of six times. Three decomposed methods, discrete wavelet transform (DWT), empirical mode decomposition (EMD), and ensemble EMD (EEMD), were used to sense the progression of muscle fatigue. They compared with each other. Although the highest frequency components of sEMG by DWT, EMD, and EEMD have the better performance to sense the progression of muscle fatigue than the raw sEMG, the EEMD has the best performance to reduce nonstationary characteristics and noise of the dynamic sEMG.

Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.

Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

Muscle fatigue during cross country sprint assessed by activation patterns and electromyographic signals time–frequency analysis

The aims of this study were as follows: (i) analysis of activation patterns during the spurt of two heats of a cross country skiing sprint with the double poling technique and (ii) quantification of muscle fatigue of the investigated muscles. Eight elite skiers were tested and surface electromyographic signals (EMG) were recorded from seven muscles of the upper and lower limbs. For each subject and each muscle, the activation intervals were calculated for relying on a double-threshold statistical detector and the average rectified value was calculated on each activation interval. The detected activations were processed by a time-frequency algorithm in order to assess the progression of muscle fatigue. The EMG activation patterns and EMG amplitude highlighted no significant difference between the two spurts, despite a generally lower speed in the second spurt. The frequency analysis showed that upper body muscles are the first to be affected by fatigue and that clear signs of muscle fatigue appear right from the first spurt of the sprint simulation (i.e., biceps and triceps brachii) with a decrease in the instantaneous mean frequency. Biceps brachii activations and fatigue demonstrated the involvement of this muscle in propulsion.

Mechanical and Metabolic Responses with Exercise and Dietary Carbohydrate Manipulation

To investigate the effects of altered muscle glycogen content on the mechanical and metabolic responses to prolonged exercise of moderate intensity. Eight volunteers (.VO2peak = 49.3 +/- 1.2 mL.kg(-1).min(-1)) cycled to fatigue on two occasions: after a 3-d low-carbohydrate diet (Lo CHO), which had been preceded by glycogen-depleting exercise, and then after a 3-d high-carbohydrate diet (Hi CHO). Metabolic and mechanical properties were assessed at both Lo CHO and Hi CHO before exercise (Pre), at 30 min of exercise (30 min), at fatigue in Lo CHO (Post 1), and again at fatigue after a brief rest (Post 2). For the Lo CHO cycle, time to fatigue averaged 66.7 +/- 4.5 and 9.5 +/- 1.7 min for Post 1 and Post 2, respectively. For Hi CHO, Post 2 time to fatigue was 64.9 +/- 6.3 min. Muscle glycogen was elevated (P < 0.05) by approximately 40% in Hi CHO compared with Lo CHO. Phosphocreatine, although higher (P < 0.05) by approximately 25% during exercise in Hi CHO, was not different at Pre. Similar but reciprocal effects (P < 0.05) were observed for inorganic phosphate and creatine. Force at low frequencies of stimulation was maximally reduced (P < 0.05) by approximately 26-38% by 30 min of exercise, regardless of condition. A 7-d exercise-dietary protocol leads to both an elevation in muscle glycogen and improved energy homeostasis during exercise. Although these adaptations may explain the improved cycle performance, they are not related to the progression of muscle fatigue as assessed statically at low frequencies of stimulation.

Electrical Manifestations of Muscle Fatigue During Concentric and Eccentric Isokinetic Knee Flexion-Extension Movements

The quantification of the progression of muscle fatigue during a sustained contraction is a valuable tool in several clinical applications, ranging from the evaluation of functional impairment to the development of specific rehabilitative and training protocols. In these fields, great importance is given to isokinetic contractions. The aim of this paper was twofold: first, to propose signal processing methods for assessing the spectral changes of the surface myoelectric signal due to fatigue during isokinetic concentric and eccentric knee flexion-extension movements at a given angular velocity (60 degrees/s); second, to analyze the electrical manifestations of muscle fatigue of four thigh muscles (vastus lateralis, vastus medialis, rectus femoris, and biceps femoris) in the two contraction modalities (i.e. concentric versus eccentric). We demonstrated that, when considering concentric contractions, localized muscle fatigue can be assessed by computing the mean frequency of the frequency marginal of the time-frequency distribution derived from the surface myoelectric signal collected during each contraction cycle. Stronger nonstationarities were observed in the surface myoelectric data recorded within each cyclical movement of the studied eccentric exercise. Thus we propose the computation of the instantaneous mean frequency of the signal based on an original cross-time-frequency algorithm, which proved more sensitive than the frequency marginal in tracking the spectral changes associated with localized muscle fatigue. We derived the average fatigue pattern of the investigated muscles from experimental data recorded from a sample population consisting of twenty healthy subjects and we statistically compared the two contraction modalities. Our results showed that the electrical manifestations of muscle fatigue during concentric contractions were higher than those found during eccentric contractions, although in the latter modality the torque exerted and the mechanical work produced by the subjects were larger than those recorded during the concentric exercise. The results presented in this paper have potential clinical application and they could play an important future role in investigations of muscle behavior during dynamic, highly fatiguing contractions.

Baroreflex resetting during exercise: mechanisms and meaning

the cardiovascular adjustments to exercise constitute well- coordinated responses throughout the body. Virtually every organ system is required to do more (e.g., skeletal muscle, the respiratory pump, and the cardiac pump) or to do the same as at rest while receiving less blood flow. Of all the

9 Regulation of Muscle Sympathetic Nerve Activity During Exercise in Humans

Recent investigations using direct (microneurographic) recordings of MSNA have provided a substantial amount of new information on the regulation of sympathetic nervous system control of nonactive skeletal muscle blood flow during exercise in humans. Some of the new conclusions from these studies discussed in this review include: 1. The direction, pattern and magnitude of the MSNA response to exercise depend on the collective influence of a number of factors, including the mode (isometric or rhythmic), intensity, and duration of the exercise, the size of the contracting muscle mass, and possibly the level of conditioning (physical training) of the exercising muscles. The MSNA response also appears to be tightly coupled with the onset and progression of muscle fatigue, at least during sustained, isometric contractions. 2. Increases in MSNA evoked during exercise with the arms are fairly uniform among different skeletal muscle nerves, and these responses correlate strongly with changes in venous plasma norepinephrine concentrations, limb vascular resistance and arterial blood pressure. Thus, increases in this neural activity during exercise are associated with the expected physiological responses. 3. The MSNA response to the same level of exercise varies markedly among healthy subjects but appears to be consistent over time within a particular subject. 4. The muscle metaboreflex (muscle chemoreflex) is the primary-mechanism by which MSNA is stimulated during small-muscle, isometric exercise in humans. In contrast, central command has a relatively weak influence on MSNA during this type of exercise. 5. Muscle metaboreflex-stimulation of MSNA also occurs during dynamic exercise, but only at or above moderate, submaximal intensities (i.e., not during mild exercise). 6. Muscle metaboreflex-evoked increases in MSNA during exercise are strongly associated with glycogenolysis and the consequent cellular accumulation of hydrogen ions in the contracting muscles. 7. Sympathoinhibitory cardiopulmonary reflexes do not appear to modulate the MSNA responses to isometric exercise in the healthy human. However, arterial baroreflexes exert a potent inhibitory effect on MSNA during this form of exercise. The mechanisms involved in the regulation of MSNA during large-muscle, dynamic leg exercise is an important topic for future investigations, as is the relationship between MSNA and sympathetic outflow to other regional circulations (e.g., heart, viscera, skin) during various forms of exercise.