Responses in knee joint muscle activation patterns to different perturbations during gait in healthy subjects

PurposeTo compare the responses in knee joint muscle activation patterns to different perturbations during gait in healthy subjects.ScopeNine healthy participants were subjected to perturbed walking on a split-belt treadmill. Four perturbation types were applied, each at five intensities. The activations of seven muscles surrounding the knee were measured using surface EMG. The responses in muscle activation were expressed by calculating mean, peak, co-contraction (CCI) and perturbation responses (PR) values. PR captures the responses relative to unperturbed gait. Statistical parametric mapping analysis was used to compare the muscle activation patterns between conditions.ResultsPerturbations evoked only small responses in muscle activation, though higher perturbation intensities yielded a higher mean activation in five muscles, as well as higher PR. Different types of perturbation led to different responses in the rectus femoris, medial gastrocnemius and lateral gastrocnemius. The participants had lower CCI just before perturbation compared to the same phase of unperturbed gait.ConclusionsHealthy participants respond to different perturbations during gait with small adaptations in their knee joint muscle activation patterns. This study provides insights in how the muscles are activated to stabilize the knee when challenged. Furthermore it could guide future studies in determining aberrant muscle activation in patients with knee disorders.     

The role of afferent feedback in the control of hamstrings activity during human gait

In vertebrates, possibly also in man, the pattern of activation of muscles during locomotion can be generated by the spinal cord (locomotor CPG, central pattern generator). However, sensory feedback is crucial to adapt the functioning of the CPG to the external requirements during gait. It is postulated that afferent input from skin and muscles can contribute to the EMG activation patterns as observed in various limb muscles during gait. The activity of the hamstrings at end swing may be partially due to stretch reflexes of these muscles. At end stance the hamstring activity may be assisted by reflexes from natural skin activation from the dorsum of the foot. In addition, more specific actions are also incorporated. For example, sural nerve stimulation induces an activation of biceps femoris (BF) whereas a suppression is usually obtained for semitendinosus (ST), indicating that the induced activation is aimed at exorotation of the lower leg. Similarly, the preferential activation of medial versus lateral gastrocnemius (GM versus GL) in sural nerve induced reflexes could favor such exorotation. It is concluded that the present evidence points towards a possible contribution of various reflexes to the motor output seen during gait for movements both inside and outside the sagittal plane.     

Artificial neural network model for the generation of muscle activation patterns for human locomotion.

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.     

Influence of locomotion speed on biomechanical subtask and muscle synergy

This paper investigates the relationship of biomechanical subtasks, and muscle synergies with various locomotion speeds. Ground reaction force (GRF) of eight healthy subjects is measured synchronously by force plates of treadmill at five different speeds ranging from 0.5 m/s to 1.5 m/s. Four basic biomechanical subtasks, body support, propulsion, swing, and heel strike preparation, are identified according to GRF. Meanwhile, electromyography (EMG) data, used to extract muscle synergies, are collected from lower limb muscles. EMG signals are segmented periodically based on GRF with the heel strike as the split points. Variability accounted for (VAF) is applied to determine the number of muscle synergies. We find that four muscle synergies can be extracted in all five situations by non-negative matrix factorization (NMF). Furthermore, the four muscle synergies and biomechanical subtasks keep invariant as the walking speed changes.     

Use of muscle synergies and wavelet transforms to identify fatigue during squatting

The objective of this study was to supplement continuous wavelet transforms with muscle synergies in a fatigue analysis to better describe the combination of decreased firing frequency and altered activation profiles during dynamic muscle contractions. Nine healthy young individuals completed the dynamic tasks before and after they squatted with a standard Olympic bar until complete exhaustion. Electromyography (EMG) profiles were analyzed with a novel concatenated non-negative matrix factorization method that decomposed EMG signals into muscle synergies. Muscle synergy analysis provides the activation pattern of the muscles while continuous wavelet transforms output the temporal frequency content of the EMG signals. Synergy analysis revealed subtle changes in two-legged squatting after fatigue while differences in one-legged squatting were more pronounced and included the shift from a general co-activation of muscles in the pre-fatigue state to a knee extensor dominant weighting post-fatigue. Continuous wavelet transforms showed major frequency content decreases in two-legged squatting after fatigue while very few frequency changes occurred in one-legged squatting. It was observed that the combination of methods is an effective way of describing muscle fatigue and that muscle activation patterns play a very important role in maintaining the overall joint kinetics after fatigue.     

No evidence of expertise-related changes in muscle synergies during rowing

The purpose of the present study was to determine whether expertise in rowing is driven by a specific structure in muscular coordination. We compared seven experienced rowers and eight untrained (i.e., inexperienced) subjects during rowing on an ergometer. Both surface electromyography activity and mechanical patterns (forces exerted at the handle and the foot-stretcher) were recorded during a high intensity rowing exercise. A non-negative matrix factorization was applied to 23 electromyographic patterns to differentiate muscle synergies. Results showed that expertise was not associated with different dimensionality in the electromyographic data and that three muscle synergies were sufficient to explain the majority of the variance accounted for (i.e., >90% of the total variance) in the two populations. The synergies extracted were similar in the two populations, with identical functional roles. While the temporal organization of the propulsive synergies was very similar, slight differences were found in the composition of the muscle synergies (muscle synergy vectors) between the two populations. The results suggests that rowing expertise would not require the development of novel muscle synergies but would imply intrinsic synergies already used in different behaviors. Performance in rowing is more probably linked to adjustments in the mechanical output of the muscle synergies rather than to differences in the shape and timing of their activations.     

Can altered muscle synergies control unimpaired gait?

Recent studies have postulated that the human motor control system recruits groups of muscles through low-dimensional motor commands, or muscle synergies. This scheme simplifies the neural control problem associated with the high-dimensional structure of the neuromuscular system. Several lines of evidence have suggested that neurological injuries, such as stroke or cerebral palsy, may reduce the dimensions that are available to the motor control system, and these altered dimensions or synergies are thought to contribute to impaired walking patterns. However, no study has investigated whether impaired low-dimensional control spaces necessarily lead to impaired walking patterns. In this study, using a two-dimensional model of walking, we developed a synergy-based control framework that can simulate the dynamics of walking. The simulation analysis showed that a synergy-based control scheme can produce well-coordinated movements of walking matching unimpaired gait. However, when the dimensions available to the controller were reduced, the simplified emergent pattern deviated from unimpaired gait. A system with two synergies, similar to those seen after neurological injury, could not produce an unimpaired walking pattern. These findings provide further evidence that altered muscle synergies can contribute to impaired gait patterns and may need to be directly addressed to improve gait after neurological injury.     

Relating neuromuscular control to functional anatomy of limb muscles in extant archosaurs

Electromyography (EMG) is used to understand muscle activity patterns in animals. Understanding how much variation exists in muscle activity patterns in homologous muscles across animal clades during similar behaviours is important for evaluating the evolution of muscle functions and neuromuscular control. We compared muscle activity across a range of archosaurian species and appendicular muscles, including how these EMG patterns varied across ontogeny and phylogeny, to reconstruct the evolutionary history of archosaurian muscle activation during locomotion. EMG electrodes were implanted into the muscles of turkeys, pheasants, quail, guineafowl, emus (three age classes), tinamous and juvenile Nile crocodiles across 13 different appendicular muscles. Subjects walked and ran at a range of speeds both overground and on treadmills during EMG recordings. Anatomically similar muscles such as the lateral gastrocnemius exhibited similar EMG patterns at similar relative speeds across all birds. In the crocodiles, the EMG signals closely matched previously published data for alligators. The timing of lateral gastrocnemius activation was relatively later within a stride cycle for crocodiles compared to birds. This difference may relate to the coordinated knee extension and ankle plantarflexion timing across the swing-stance transition in Crocodylia, unlike in birds where there is knee flexion and ankle dorsiflexion across swing-stance. No significant effects were found across the species for ontogeny, or between treadmill and overground locomotion. Our findings strengthen the inference that some muscle EMG patterns remained conservative throughout Archosauria: for example, digital flexors retained similar stance phase activity and M. pectoralis remained an ‘anti-gravity’ muscle. However, some avian hindlimb muscles evolved divergent activations in tandem with functional changes such as bipedalism and more crouched postures, especially M. iliotrochantericus caudalis switching from swing to stance phase activity and M. iliofibularis adding a novel stance phase burst of activity.     

A musculoskeletal foot model for clinical gait analysis

Several full body musculoskeletal models have been developed for research applications and these models may potentially be developed into useful clinical tools to assess gait pathologies. Existing full-body musculoskeletal models treat the foot as a single segment and ignore the motions of the intrinsic joints of the foot. This assumption limits the use of such models in clinical cases with significant foot deformities. Therefore, a three-segment musculoskeletal model of the foot was developed to match the segmentation of a recently developed multi-segment kinematic foot model. All the muscles and ligaments of the foot spanning the modeled joints were included. Muscle pathways were adjusted with an optimization routine to minimize the difference between the muscle flexion–extension moment arms from the model and moment arms reported in literature. The model was driven by walking data from five normal pediatric subjects (aged 10.6±1.57 years) and muscle forces and activation levels required to produce joint motions were calculated using an inverse dynamic analysis approach. Due to the close proximity of markers on the foot, small marker placement error during motion data collection may lead to significant differences in musculoskeletal model outcomes. Therefore, an optimization routine was developed to enforce joint constraints, optimally scale each segment length and adjust marker positions. To evaluate the model outcomes, the muscle activation patterns during walking were compared with electromyography (EMG) activation patterns reported in the literature. Model-generated muscle activation patterns were observed to be similar to the EMG activation patterns.     

A Proprioception Based Regulation Model to Estimate the Trunk Muscle Forces

Evaluation of loads acting on the spine requires the knowledge of the muscular forces acting on it, but muscles redundancy necessitates developing a muscle forces attribution strategy. Optimisation, EMG, or hybrid models allow evaluating muscle force patterns, yielding a unique muscular arrangement or/and requiring EMG data collection. This paper presents a regulation model of the trunk muscles based on a proprioception hypothesis, which searches to avoid the spinal joint overloading. The model is also compared to other existing models for evaluation. Compared to an optimisation model, the proposed alternative muscle pattern yielded a significant spine postero-anterior shear decrease. Compared to a model based on combination of optimisation criteria, present model better fits muscle activation observed using EMG (38% improvement). Such results suggest that the proposed model, based on regulation of all spinal components, may be more relevant from a physiologic point of view.     

Lumbar spinal muscle activation synergies predicted by multi-criteria cost function

The hypothesis that control of lumbar spinal muscle synergies is biomechanically optimized was studied by comparing EMG data with an analytical model with a multi-component cost function that could include (1) trunk displacements, (2) intervertebral displacements, (3) intervertebral forces; (4) sum of cubed muscle stresses, and (5) eigenvalues for the first two spinal buckling modes. The model's independent variables were 180 muscle forces. The 36 displacements of 6 vertebrae were calculated from muscle forces and the spinal stiffness. Calculated muscle activation was compared with EMG data from 14 healthy human subjects who performed isometric voluntary ramped maximum efforts at angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees and 180 degrees to the right from the anterior direction. Muscle activation at each angle was quantified as the linear regression slope of the RMS EMG versus external force relationship, normalized by the maximum observed EMG.There was good agreement between the analytical model and EMG data for the dorsal muscles when the model included either minimization of intervertebral displacements or minimization of intervertebral forces in its cost function, but the model did not predict a realistic level of abdominal muscles activation. Agreement with EMG data was improved with the sum of the cubed muscle stresses added to the cost function. Addition of a cost function component to maximize the trunk stability produced higher levels of antagonistic muscle activation at low efforts than at greater efforts. It was concluded that the muscle activation strategy efficiently limits intervertebral forces and displacements, and that costs of higher muscle stresses are taken into account, but stability does not appear to be maximized. Trunk muscles are apparently not controlled solely to optimize any one of the biomechanical costs considered here.     

Estimation of muscle forces in gait using a simulation of the electromyographic activity and numerical optimization

Clinical gait analysis provides great contributions to the understanding of gait patterns. However, a complete distribution of muscle forces throughout the gait cycle is a current challenge for many researchers. Two techniques are often used to estimate muscle forces: inverse dynamics with static optimization and computer muscle control that uses forward dynamics to minimize tracking. The first method often involves limitations due to changing muscle dynamics and possible signal artefacts that depend on day-to-day variation in the position of electromyographic (EMG) electrodes. Nevertheless, in clinical gait analysis, the method of inverse dynamics is a fundamental and commonly used computational procedure to calculate the force and torque reactions at various body joints. Our aim was to develop a generic musculoskeletal model that could be able to be applied in the clinical setting. The musculoskeletal model of the lower limb presents a simulation for the EMG data to address the common limitations of these techniques. This model presents a new point of view from the inverse dynamics used on clinical gait analysis, including the EMG information, and shows a similar performance to another model available in the OpenSim software. The main problem of these methods to achieve a correct muscle coordination is the lack of complete EMG data for all muscles modelled. We present a technique that simulates the EMG activity and presents a good correlation with the muscle forces throughout the gait cycle. Also, this method showed great similarities whit the real EMG data recorded from the subjects doing the same movement.     

Immediate effects of co-contraction training on motor control of the trunk muscles in people with recurrent low back pain

Although deficits in the activation of abdominal muscles are present in people with low back pain (LBP), this can be modified with motor training. Training of deep abdominal muscles in isolation from the other trunk muscles, as an initial phase of training, has been shown to improve the timing of activation of the trained muscles, and reduce symptoms and recurrence of LBP. The aim of this study was to determine if training of the trunk muscles in a non-isolated manner can restore motor control of these muscles in people with LBP. Ten subjects with non-specific LBP performed a single session of training that involved three tasks: “abdominal curl up”, “side bridge” and “birdog”. Electromyographic activity (EMG) of trunk and deltoid muscles was recorded with fine-wire and surface electrodes during rapid arm movements and walking, before and immediately following the intervention. Onset of trunk muscle EMG relative to that of the prime mover (deltoid) during arm movements and the mean, standard deviation (SD) and coefficient of variation of abdominal muscle EMG during walking were calculated. There was no significant change in the times of onset of trunk muscle EMG during arm movements nor was there any change in the variability of EMG of the abdominal muscles during walking. However, the mean amplitude and SD of abdominal EMG was reduced during walking after training. The results of this study suggest that unlike isolated voluntary training, co-contraction training of the trunk muscles does not restore the motor control of the deep abdominal muscles in people with LBP after a single session of training.     

An EMG-CT method using multiple surface electrodes in the forearm

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image. For validation of the method, isometric contractions of finger muscles were examined for three subjects, applying a flexion load (4.9, 7.4 and 9.8 N) to the proximal interphalangeal joint of the middle finger. EMG signals in the forearm were recorded during the tasks using multiple surface electrodes, which were bound around the subject’s forearm. The EMG-CT method illustrates the distribution of muscle activities within the forearm. The change in amplitude and area of activated muscles can be observed. The normalized muscle activities of all three subjects appear to increase monotonically with increases in the load. Kinesiologically, this method was able to estimate individual muscle activation values and could provide a novel tool for studying hand function and development of an examination for evaluating rehabilitation.     

Trunk and hip muscle activity in early walkers with and without cerebral palsy – A frequency analysis

Poor control of postural muscles is a primary impairment in cerebral palsy (CP), yet core trunk and hip muscle activity has not been thoroughly investigated. Frequency analysis of electromyographic (EMG) signals provides insight about the intensity and pattern of muscle activation, correlates with functional measures in CP, and is sensitive to change after intervention. The objective of this study was to investigate differences in trunk and hip muscle activation frequency in children with CP compared to children with similar amounts of walking experience and typical development (TD). EMG data from 31 children (15 with CP, 16 with TD) were recorded from 16 trunk and hip muscles bilaterally. A time–frequency pattern was generated using the continuous wavelet transform and instantaneous mean frequency (IMNF) was calculated at each interval of the gait cycle. Functional principal component analysis (PCA) revealed that IMNF was significantly higher in the CP group throughout the gait cycle for all muscles. Additionally, stride-to-stride variability was higher in the CP group. This evidence demonstrated altered patterns of trunk and hip muscle activation in CP, including increased rates of motor unit firing, increased number of recruited motor units, and/or decreased synchrony of motor units. These altered muscle activation patterns likely contribute to muscle fatigue and decreased biomechanical efficiency in children with CP.     

Muscle synergies during bench press are reliable across days

Muscle synergies have been investigated during different types of human movement using nonnegative matrix factorization. However, there are not any reports available on the reliability of the method. To evaluate between-day reliability, 21 subjects performed bench press, in two test sessions separated by approximately 7 days. The movement consisted of 3 sets of 8 repetitions at 60% of the three repetition maximum in bench press. Muscle synergies were extracted from electromyography data of 13 muscles, using nonnegative matrix factorization. To evaluate between-day reliability, we performed a cross-correlation analysis and a cross-validation analysis, in which the synergy components extracted in the first test session were recomputed, using the fixed synergy components from the second test session. Two muscle synergies accounted for >90% of the total variance, and reflected the concentric and eccentric phase, respectively. The cross-correlation values were strong to very strong (r-values between 0.58 and 0.89), while the cross-validation values ranged from substantial to almost perfect (ICC3, 1 values between 0.70 and 0.95). The present findings revealed that the same general structure of the muscle synergies was present across days and the extraction of muscle synergies is thus deemed reliable.     

Coordinated network functioning in the spinal cord: An evolutionary perspective

The successful achievement of harmonious locomotor movement results from the integrated operation of all body segments. Here, we will review current knowledge on the functional organization of spinal networks involved in mammalian locomotion. Attention will not simply be restricted to hindlimb muscle control, but by also considering the necessarily coordinated activation of trunk and forelimb muscles, we will try to demonstrate that while there has been a progressive increase in locomotor system complexity during evolution, many basic organizational features have been preserved across the spectrum from lower vertebrates through to humans. Concerning the organization of axial neuronal networks that control trunk muscles, it has been found across the vertebrate range that during locomotor movement a motor wave travels longitudinally in the spinal cord via the coupling of rhythmic segmental networks. For hindlimb activation it has been found in all species studied that the rostral lumbar segments contain the key elements for pattern generation. We also showed that rhythmic arm movements are under the control of cervical forelimb generators in quadrupeds as well as in human. Finally, it is highlighted that the coordination of quadrupedal movements during locomotion derives principally from an asymmetrical coordinating influence occurring in the caudo-rostral direction from the lumbar hindlimb networks.     

Contribution of stretch reflexes to locomotor control: a modeling study

It is known that the springlike properties of muscles provide automatic load compensation during weight bearing. How crucial is sensory control of the motor output given these basic properties of the locomotor system? To address this question, a neuromuscular model was used to test two hypotheses. (1) Stretch reflexes are too weak and too delayed to contribute significantly to weight-bearing. (2) The important contributions of sensory input involve state-dependent processing. We constructed a two-legged planar locomotor model with 9 segments, driven by 12 musculotendon actuators with Hill-type force-velocity and monotonic force-length properties. Electromyographic (EMG) profiles of the simulated muscle groups during slow level walking served as actuator activation functions. Spindle Ia and tendon organ Ib sensory inputs were represented by transfer functions with a latency of 35 ms, contributing 30% to the net EMG profile and gated to be active only when the receptor-bearing muscles were contracting. Locomotor stability was assessed by parametric variations of actuator maximum forces during locomotion in open-loop ("deafferented") trials and in trials with feedback control based on either sensory-evoked stretch reflexes or finite-state rules. We arrived at the following conclusions. (1) In the absence of sensory control, the intrinsic stiffness of limb muscles driven by a stereotyped rhythmical pattern can produce surprisingly stable gait. (2) When the level of central activity is low, the contribution of stretch reflexes to load compensation can be crucial. However, when central activity provides adequate load compensation, the contribution of stretch reflexes is less significant. (3) Finite-state control can greatly extend the adaptive capability of the locomotor system.     

An inter-segmental network model and its use in elucidating gait-switches in the stick insect

Animal locomotion requires highly coordinated working of the segmental neuronal networks that control the limb movements. Experiments have shown that sensory signals originating from the extremities play a pivotal role in controlling locomotion patterns by acting on central networks. Based on the results from stick insect locomotion, we constructed an inter-segmental model comprising local networks for all three legs, i.e. for the pro-, meso- and meta-thorax, their inter-connections and the main sensory inputs modifying their activities. In the model, the local networks are uniform, and each of them consists of a central pattern generator (CPG) providing the rhythmic oscillation for the protractor-retractor motor systems, the corresponding motoneurons (MNs), and local inhibitory interneurons (IINs) between the CPGs and the MNs. Between segments, the CPGs are connected cyclically by both excitatory and inhibitory pathways that are modulated by the aforementioned sensory inputs. Simulations done with our network model showed that it was capable of reproducing basic patterns of locomotion such as those occurring during tri- and tetrapod gaits. The model further revealed a number of elementary neuronal processes (e.g. synaptic inhibition, or changing the synaptic drive at specific neurons) that in the simulations were necessary, and in their entirety sufficient, to bring about a transition from one type of gait to another. The main result of this simulation study is that exactly the same mechanism underlies the transition between the two types of gait irrespective of the direction of the change. Moreover, the model suggests that the majority of these processes can be attributed to direct sensory influences, and changes are required only in centrally controlled synaptic drives to the CPGs.