Increase in heterogeneity of biceps brachii activation during isometric submaximal fatiguing contractions: a multichannel surface EMG study

Exploring the acute muscle fatigue response in resistance trained individuals during eccentric quasi-isometric elbow flexions-a cross-sectional comparison of repetition and sex

Eccentric quasi-isometrics (EQIs) are a novel, low-velocity resistance exercise technique that incorporates a holding isometric contraction to positional fatigue, followed by voluntary resistance of the resulting eccentric muscle action. As females are typically more fatigue resistant than males during isometric and low-velocity dynamic muscle actions, this study explored sex-differences in the muscle fatigue response to an EQI protocol. Twenty-five (n = 12 female) participants completed 4 unilateral EQI elbow flexions. Absolute and relative surface electromyography (sEMG) amplitude (iEMG, LE peak), mean power frequency (MPF), angular impulse (aIMP), and elbow angle were compared across repetitions and between sexes using discrete values and statistical parametric/non-parametric mapping. There were significant and substantial sex and repetition differences in absolute iEMG, MPF, and aIMP, however, males and females had statistically similar absolute aIMP by repetition 4. When expressed relatively, there were no significant sex-differences. Additionally, there were significant between repetition changes in sEMG amplitude and elbow angle with an increasing number of repetitions, largely in the first-two thirds of repetition time. The current study suggests that there are absolute, but not relative sex-differences in EQI induced muscle fatigue, and the effects across repetitions occur predominately in the first two-thirds of repetition time.

Advances in EMG measurement techniques, analysis procedures, and the impact of muscle mechanics on future requirements for the methodology

Muscular coordination enables locomotion and interaction with the environment. For more than 50 years electromyography (EMG) has provided insights into the central nervous system control of individual muscles or muscle groups, enabling both fine and gross motor functions. This information is available either at individual motor units (Mus) level or on a more global level from the coordination of different muscles or muscle groups. In particular, non-invasive EMG methods such as surface EMG (sEMG) or, more recently, spatial mapping methods (High-Density EMG - HDsEMG) have found their place in research into biomechanics, sport and exercise, ergonomics, rehabilitation, diagnostics, and increasingly for the control of technical devices. With further technical advances and a growing understanding of the relationship between EMG and movement task execution, it is expected that with time, especially non-invasive EMG methods will become increasingly important in movement sciences. However, while the total number of publications per year on non-invasive EMG methods is growing exponentially, the number of publications on this topic in journals with a scope in movement sciences has stagnated in the last decade. This review paper contextualizes non-invasive EMG development over the last 50 years, highlighting methodological progress. Changes in research topics related to non-invasive EMG were identified. Today non-invasive EMG procedures are increasingly used to control technical devices, where muscle mechanics have a minor influence. In movement science, however, the effect of muscle mechanics on the EMG signal cannot be neglected. This explains why non-invasive EMG's relevance in movement sciences has not developed as expected.

Activity Distribution Among the Hamstring Muscles During the Nordic Hamstring Exercise: A Multichannel Surface Electromyography Study

This study assessed activity distribution among the hamstring muscles during the Nordic hamstring exercise (NHE). The objective was to compare muscle activity between and within muscles during the NHE to add insights in its underlying protective mechanism. Through multichannel electromyography, we measured muscle activity in male basketball players during the NHE. Electromyography was assessed at 15 locations: 5 for biceps femoris long head, 4 for semitendinosus, and 6 for semimembranosus. For each percent of the eccentric phase of the NHE, muscle activity was calculated for each electrode location within each hamstring muscle individually. To quantify whole muscle head activity, means and variances across electrodes within each muscle were calculated. Thirty-five noninjured participants were included (mean age, 18 [2] y; mass, 87 [12] kg; height, 192 [9] cm). Heterogeneous muscle activity was found between 38% and 62% and over the whole eccentric contraction phase within the semitendinosus and the semimembranosus, respectively. Muscle activity of the semitendinosus was significantly higher than that of the biceps femoris long head. During the NHE, the relative contribution of the semitendinosus is the highest among hamstring muscles. Its strong contribution may compensate for the biceps femoris long head, the most commonly injured hamstring muscle head.

Investigation on the Contributions of Different Muscles to the Generated Force based on HD-sEMG and DBN

In this study, we proposed a multi-muscle contraction force estimation framework and implemented it on the elbow flexion task to explore the contributions of different muscles to integrated force at different force levels. High density surface electromyography (HD-sEMG) signals were collected from four muscle groups and the wrist pulling force was measured synchronously. The deep belief network (DBN) was adopted to establish the relationship between HD-sEMG and force. The representative signals of four primary areas, which were considered as the input signal of the force estimation model, were extracted from HD-sEMG by principle component analysis (PCA) algorithm, then fed separately or in common to the DBN to estimate the generated force. And the contributions of different muscle groups to the generated force was analyzed with an index called mean impact value (MIV). The experimental result demonstrates that in multi-muscle contraction task, not all muscles are suitable for force estimation, the force estimation accuracy obtained using only one muscle approximates even exceeds that obtained using multiple muscles, and the relative contributions of different muscle groups to the force can be obtained according to the ranking of MIVs. This scheme provides an effective method for muscle force estimation in multi-muscle contraction tasks, and can be further applied to biomechanics, sports and rehabilitation medicine.

A Novel HD-sEMG Preprocessing Method Integrating Muscle Activation Heterogeneity Analysis and Kurtosis-Guided Filtering for High-Accuracy Joint Force Estimation

This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.

HD-sEMG-based research on activation heterogeneity of skeletal muscles and the joint force estimation during elbow flexion

OBJECTIVE

To investigate the activation heterogeneity of skeletal muscles and realize the joint force estimation during the elbow flexion task.

APPROACH

When an isometric elbow flexion task was performed, high-density surface electromyography (HD-sEMG) signals from a [Formula: see text] grid covering the front and inside of the upper arm and the generated joint force were recorded synchronously. HD-sEMG signals were preprocessed and then decomposed into source signals corresponding to biceps brachhi (BB) and brachialis (BR) and their contribution vectors using a fast, independent component analysis (FastICA) algorithm. The activation heterogeneity of BB and BR was investigated from the activation level and activation region, initially. Then, the contribution combinations of two sources were classified into several major clusters using the K-means clustering method. Afterwards, input signals for force estimation were extracted from the major clusters corresponding to different combinations, and the polynomial fitting technique was adopted as the force estimation model. Finally, the force estimation results were obtained and the analysis around the force estimation performance using different input signals was conducted.

MAIN RESULTS

Ten subjects were recruited in this research. The experimental results demonstrated that it is feasible to analyze the activation heterogeneity of muscles from the activation level and activation region, and to select the appropriate region of the HD-sEMG grid for high performance force estimation. For the isometric elbow flexion task, joint force estimation accuracy could be improved when the input signal was extracted from the specific area where the contribution difference of BB and BR to the HD-sEMG signals were relatively small.

SIGNIFICANCE

The proposed framework provided a novel way to explore the relationship between muscle activation and the generating joint force, and could be extended to multiple noteworthy research fields such as myoelectric prostheses, sports biomechanics, and muscle disease diagnosis.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

OBJECTIVE

To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle.

APPROACH

Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique.

MAIN RESULTS

Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number.

SIGNIFICANCE

The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Relationship between Isometric Muscle Force and Fractal Dimension of Surface Electromyogram

The relationship between fractal dimension of the surface electromyogram (sEMG) and the intensity of muscle contraction is still controversial in simulated and experimental conditions. To support the use of fractal analysis to investigate myoelectric fatigue, it is crucial to establish the interdependence between fractal dimension and muscle contraction intensity. We analyzed the behavior of fractal dimension, conduction velocity, mean frequency, and average rectified value in twenty-eight volunteers at nine levels of isometric force. sEMG was obtained using bidimensional arrays in the biceps brachii muscle. The values of fractal dimension and mean frequency increased with force unless a plateau was reached at 30% maximal voluntary contraction. Overall, our findings suggest that, above a certain level of force, the use of fractal dimension to evaluate the myoelectric manifestations of fatigue may be considered, regardless of muscle contraction intensity.

Ankle muscle tenotomy does not alter ankle flexor muscle recruitment bias during locomotor-related repetitive limb movement in late-stage chick embryos

In ovo, late-stage chick embryos repetitively step spontaneously, a locomotor-related behavior also identified as repetitive limb movement (RLM). During RLMs, there is a flexor bias in recruitment and drive of leg muscle activity. The flexor biased activity occurs as embryos assume an extremely flexed posture in a spatially restrictive environment 2-3 days before hatching. We hypothesized that muscle afferent feedback under normal mechanical constraint is a significant input to the flexor bias observed during RLMs on embryonic day (E) 20. To test this hypothesis, muscle afference was altered either by performing a tenotomy of ankle muscles or removing the shell wall restricting leg movement at E20. Results indicated that neither ankle muscle tenotomy nor unilateral release of limb constraint by shell removal altered parameters indicative of flexor bias. We conclude that ankle muscle afference is not essential to ankle flexor bias characteristic of RLMs under normal postural conditions at E20.

Differences in flexor and extensor activity during locomotor-related leg movements in chick embryos

Prior to hatching, chick embryos spontaneously produce repetitive limb movements (RLMs), a developmental precursor to walking. During RLMs, flexor and extensor muscles are alternately active as during stance and swing phases of gait. However, previous studies of RLMs observed that flexor muscles were rhythmically active for many cycles, whereas extensors often failed to be recruited. Thus, we asked if flexor muscles are preferentially recruited during RLMs in chick embryos 1 day before hatching and onset of walking. Using a within-subject design, we compared EMG burst parameters for flexor and extensor muscles acting at the hip or ankle. Findings indicated that flexor burst count exceeded extensor count. Also, flexor muscles were consistently recruited at the lowest levels of neural drive. We conclude that there is a bias favoring flexor muscle recruitment and drive during spontaneously produced RLMs. Potential neural mechanisms and developmental implications of our findings are discussed.

Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury

OBJECTIVE

The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI).

APPROACH

Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine.

MAIN RESULTS

Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results.

SIGNIFICANCE

Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intention.

Force Modelling of Upper Limb Biomechanics Using Ensemble Fast Orthogonal Search on High-Density Electromyography

An important quality of upper limb force estimation is the repeatability and worst-case performance of the estimator. The following paper proposes a methodology using an ensemble learning technique coupled with the fast orthogonal search (FOS) algorithm to reliably predict varying isometric contractions of the right arm. This method leverages the rapid and precise modelling offered by FOS combined with a univariate outlier detection algorithm to dynamically combine the output of numerous FOS models. This is performed using high-density surface electromyography (HD-SEMG) obtained from three upper-arm muscles, the biceps brachii, triceps brachii and brachioradialis. This method offers improved performance over other HD-SEMG and SEMG based force estimators, with a substantial reduction in the number of channels required.

The extraction of neural strategies from the surface EMG: an update

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486-1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue.