Relative contributions of the long and short heads of the biceps brachii during single or dual isometric tasks

Identification of EIMD Level Differences Between Long- and Short Head of Biceps Brachii Using Echo Intensity and GLCM Texture Features

Purpose: This study aimed to compare the time-course changes of exercise-induced muscle damage (EIMD) levels in the long head of biceps brachii (LHB) and short head of the biceps brachii (SHB) using echo intensity (EI) and to determine the efficiency of the gray level co-occurrence matrix (GLCM) texture parameters. Methods: The participants performed 30 maximal eccentric contractions of the elbow flexor. Along with muscle damage indicators, including circumference, range of motion, muscle soreness, and maximal voluntary isometric contraction (MVIC), the EI and GLCM texture features of the LHB and SHB was also assessed using B-mode ultrasonography. All measurements were assessed pre- and immediately post-exercise and after 24, 48, 72, and 96 h. Results: The muscle damage indicators indicated significant changes after the eccentric contractions (p < 0.01 for circumference, range of motion, muscle soreness, and MVIC). The EI of LHB significantly increased following the contractions (p < 0.01), but that of SHB did not (p > 0.05). In contrast, for the GLCM texture parameters, there were significant changes in the SHB (p < 0.01 for homogeneity, energy, and entropy). Conclusion: Thus, this study demonstrated that EIMD severity is different between LHB and SHB even within the same muscle. In the GLCM features, the time course of SHB after eccentric contraction revealed different patterns compared with those of LHB. Therefore, even if there are no changes in EI within a target muscle following muscle contractions, new information on muscle quality can be obtained through GLCM analysis.

Modelling motor units in 3D: influence on muscle contraction and joint force via a proof of concept simulation

Functional heterogeneity is a skeletal muscle’s ability to generate diverse force vectors through localised motor unit (MU) recruitment. Existing 3D macroscopic continuum-mechanical finite element (FE) muscle models neglect MU anatomy and recruit muscle volume simultaneously, making them unsuitable for studying functional heterogeneity. Here, we develop a method to incorporate MU anatomy and information in 3D models. Virtual fibres in the muscle are grouped into MUs via a novel “virtual innervation” technique, which can control the units’ size, shape, position, and overlap. The discrete MU anatomy is then mapped to the FE mesh via statistical averaging, resulting in a volumetric MU distribution. Mesh dependency is investigated using a 2D idealised model and revealed that the amount of MU overlap is inversely proportional to mesh dependency. Simultaneous recruitment of a MU’s volume implies that action potentials (AP) propagate instantaneously. A 3D idealised model is used to verify this assumption, revealing that neglecting AP propagation results in a slightly less-steady force, advanced in time by approximately 20 ms, at the tendons. Lastly, the method is applied to a 3D, anatomically realistic model of the masticatory system to demonstrate the functional heterogeneity of masseter muscles in producing bite force. We found that the MU anatomy significantly affected bite force direction compared to bite force magnitude. MU position was much more efficacious in bringing about bite force changes than MU overlap. These results highlight the relevance of MU anatomy to muscle function and joint force, particularly for muscles with complex neuromuscular architecture.

High dimensional feature data reduction of multichannel sEMG for gesture recognition based on double phases PSO

Surface electromyography (sEMG) is a kind of valuable bioelectric signal and very potential in the field of human–machine interaction. Ideal interactions require sEMG based patterns recognition not only with high accuracy but also with good rapidity. However, too much real-time feature-related computation will greatly slow down the interaction, especially for multichannel sEMG. To decrease the feature-related time consumption, the paper formulates the feature reduction as an optimization problem, and develops a double-phases particle swarm optimization (PSO) with hybrid coding to solve the problem. In the research, the initial feature data set with 31 kinds of feature is built firstly based on eight subjects’ 16 channels forearm sEMG signals, then PSO is introduced to conduct the feature reduction of 31 × 16 dimensions through the feature and channel optimization in double phases. During the optimization, two improved k-nearest neighbor (KNN) methods such as weighted representation based KNN (WRKNN) and weighted local mean representation based KNN (WLMRKNN) are introduced to classify the gestures, and the classification accuracy is used to evaluate the particles of PSO. Experimental results and comparison analysis show that PSO based feature reduction methods outperform genetic algorithm (GA), ant colony optimization (ACO) and principal component analysis (PCA) based feature reduction methods. With the optimized feature data subset by PSO, WRKNN and WLMRKNN are superior to KNN, quadratic discriminant analysis (QDA), and naive bayes (NB) greatly. The proposed method can be applied in the pattern recognition of high dimensional sEMG with multichannel or high-density channels for the purpose of rapidity and without a decline of accuracy in real-time control. Further, it can be used to reduce the economic cost of the personalized customization equipment through the optimal channels for any subjects in the future.

Differential Activation of Biceps Brachii Muscle Compartments for Human-Machine Interfacing

A central challenge for myoelectric limb prostheses resides in the fact that, as the level of amputation becomes more proximal, the number of functions to be replaced increases, while the number of muscles available to collect input signals for control decreases. Differential activation of compartments from a single muscle could provide additional control sites. However, such feat is not naturally under voluntary control. In this study, we investigated the feasibility of learning to differentially activate the two heads of the bicep brachii muscle (BBM), by using biofeedback via high-density surface electromyography (HD-sEMG). Using a one degree of freedom Fitts' law test, we observed that eight subjects could learn to control the center of gravity of BBM's myoelectric activity. In addition, we examined the activations patterns of BBM that allow for the decoding of distal hand movements. These patterns were found highly individual, but different enough to allow for decoding of motor volition of distal joints. These findings represent promising venues to increase the functionality of myoelectrically controlled upper limb prostheses.

Trainability of muscular activity level during maximal voluntary co-contraction: comparison between bodybuilders and nonathletes

Antagonistic muscle pairs cannot be fully activated simultaneously, even with maximal effort, under conditions of voluntary co-contraction, and their muscular activity levels are always below those during agonist contraction with maximal voluntary effort (MVE). Whether the muscular activity level during the task has trainability remains unclear. The present study examined this issue by comparing the muscular activity level during maximal voluntary co-contraction for highly experienced bodybuilders, who frequently perform voluntary co-contraction in their training programs, with that for untrained individuals (nonathletes). The electromyograms (EMGs) of biceps brachii and triceps brachii muscles during maximal voluntary co-contraction of elbow flexors and extensors were recorded in 11 male bodybuilders and 10 nonathletes, and normalized to the values obtained during the MVE of agonist contraction for each of the corresponding muscles (% EMGMVE). The involuntary coactivation level in antagonist muscle during the MVE of agonist contraction was also calculated. In both muscles, % EMGMVE values during the co-contraction task for bodybuilders were significantly higher (P<0.01) than those for nonathletes (biceps brachii: 66±14% in bodybuilders vs. 46±13% in nonathletes, triceps brachii: 74±16% vs. 57±9%). There was a significant positive correlation between a length of bodybuilding experience and muscular activity level during the co-contraction task (r = 0.653, P = 0.03). Involuntary antagonist coactivation level during MVE of agonist contraction was not different between the two groups. The current result indicates that long-term participation in voluntary co-contraction training progressively enhances muscular activity during maximal voluntary co-contraction.

Experimental and modelling investigation of surface EMG spike analysis

A pattern classification method based on five measures extracted from the surface electromyographic (sEMG) signal is used to provide a unique characterization of the interference pattern for different motor unit behaviours. This study investigated the sensitivity of the five sEMG measures during the force gradation process. Tissue and electrode filtering effects were further evaluated using a sEMG model. Subjects (N=8) performed isometric elbow flexion contractions from 0 to 100% MVC. The sEMG signals from the biceps brachii were recorded simultaneously with force. The basic building block of the sEMG model was the detection of single fibre action potentials (SFAPs) through a homogeneous, equivalent isotropic, infinite volume conduction medium. The SFAPs were summed to generate single motor unit action potentials. The physiologic properties from a well-known muscle model and motor unit recruitment and firing rate schemes were combined to generate synthetic sEMG signals. The following pattern classification measures were calculated: mean spike amplitude, mean spike frequency, mean spike slope, mean spike duration, and the mean number of peaks per spike. Root-mean-square amplitude and mean power frequency were also calculated. Taken together, the experimental data and modelling analysis showed that below 50% MVC, the pattern classification measures were more sensitive to changes in force than traditional time and frequency measures. However, there are additional limitations associated with electrode distance from the source that must be explored further. Future experimental work should ensure that the inter-electrode distance is no greater than 1cm to mitigate the effects of tissue filtering.

Neuromuscular efficiency of the rectus abdominis differs with gender and sport practice

The purpose of this investigation was to distinguish the abilities of the rectus abdominis (RA) muscle according to gender and sport training by means of neuromuscular parameters extracted from electromyography (EMG)-torque relationships. Thirty-eight healthy students, divided into 4 groups (i.e., 8 male runners, 10 female gymnasts, 12 male controls, and 8 female controls) were asked to perform 6 seconds of isometric trunk flexions at 20%, 25%, 75%, and 100% of their maximal voluntary contraction. Flexion torque and surface EMG of the RA muscle were recorded simultaneously to construct a EMG-torque relationship. Under maximal and submaximal conditions, an index of neuromuscular efficiency (NME) was determined to characterize the capacity of the RA muscle to develop a torque. At each level of contraction, the area of data scattering (ADS), reflecting torque and EMG fluctuations, was computed to express the capacity to maintain a constant target torque. Flexion torque, NME, and ADS values differed significantly between genders, but when data were related to anthropometric characteristics, no difference was observed. Although runners were not distinguished from male controls, gymnasts had higher flexion torque, higher NME, and lower ADS values than female controls had. These differences should reflect neural and muscular adaptations linked to the specificity of gymnastic training. These findings revealed different functional abilities of the RA muscle, according to gender and sport practices. The indices of neuromuscular capacities used in this study could constitute complementary tools to athletic trainers and professionals in sports medicine for evaluating and following, during sport-specific training programs, the abdominal muscle performance implied in force transfers with a lower cost and lower risks of back pain.

Differential activation of regions within the biceps brachii muscle during fatigue

AIM

To examine the occurrence of repeated differential activation between the heads of the biceps brachii muscle and its relation to fatigue prevention during a submaximal contraction.

METHODS

Thirty-nine subjects carried out an isometric contraction of elbow flexion at 25% of maximal voluntary contraction (MVC) until exhaustion. A grid of 13 by 10 electrodes was used to record surface electromyographic signals from both heads of the biceps brachii. The root-mean-square of signals recorded from electrodes located medially and laterally was used to analyse activation differences. Differential activation was defined as periods of 33% different activation level between the two heads of the biceps brachii muscle.

RESULTS

Differential muscle activation was demonstrated in 30 of 33 subjects with appropriate data quality. The frequency of differential activation increased from 4.9 to 6.6 min(-1) at the end of the contractions with no change in duration of the differential activations (about 1.4 s). Moreover, the frequency of differential activation was, in general, negatively correlated with time to exhaustion.

CONCLUSION

The observed differential activation between the heads of the biceps brachii can be explained by an uneven distribution of synaptic input to the motor neurone pool. The findings of this study indicate that differential activation of regions within a muscle does not prevent fatigue at a contraction level of 25% of MVC.

Motor unit recruitment and EMG power spectra during ramp contractions of a bifunctional muscle

Surface electromyograms (EMGs) were analysed on the short and long head of the biceps brachii (BBSH and BBLH) during single (F and S) or dual (F+S) flexion and supination tasks. It was confirmed, by the analysis of EMG root-mean-square (RMS) values, that the highest activations of BBSH and BBLH were obtained during a maximal dual task. This study was essentially concerned with the analysis of power spectra data obtained during progressive or ramp contractions (RCs). The shape of the power spectra established during the first second of the RCs differs between F, S and F+S tasks. Differences in mean power frequency (MPF) calculated during RCs would be representative of a recruitment of motor units (MUs) that is, at least partly, task-dependent. In order to compare MPF values calculated from RCs performed under different mechanical conditions (F, S and F+S), MPF-RMS(PSD) relationships have been established (RMS(PSD) being defined as the power spectrum density RMS). Both BBSH and BBLH exhibited initial MPF values higher in supination RC than in flexion RC. Because of plateau values reached at the same level of muscle activation whatever the task performed, the slope of the MPF-RMS(PSD) relationship was lower in S than in F. These results are in favour of MU recruitment that is, at least partly, different in F and in S conditions. Dual submaximal tasks seem to mix the activation of the F and S subpopulations of MUs as revealed by the spectral parameters obtained during F+S ramp contractions. This study could find some implication in the field of muscle rehabilitation or reinforcement.