Phasic behavior of EMG signals during gait: Use of multivariate statistics

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

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.

Monitoring 23-year of shoreline changes of the Zengwun Estuary in Southern Taiwan using time-series Landsat data and edge detection techniques

More than 80% of sandy beaches in Taiwan have been experiencing a severe recession, although the sediment discharge of rivers in Taiwan is significantly higher than the world average producing almost 2% of global fluvial sediment discharge. This contradiction is primarily due to the widespread constructions of reservoirs and intensive anthropogenic activities in coastal regions. In addition, coasts are particularly vulnerable to hazards due to climate change, such as sea-level rise, as they are located at the transition zone of terrestrial and marine environments. Along with the fact that Taiwan is an island and is one of the most climate-vulnerable regions globally, coastal management and sustainability are nationally critical topics, especially considering the ongoing reformation and legislation of Taiwan's coastal conservation laws. As stated in the Sustainable Development Goals (SDGs) goal 14, accurate and continuous shoreline positions information is essential for coastal conservation. However, by reviewing previous global studies and projects commissioned by the Taiwanese government aiming at monitoring shoreline changes, they usually exhibit several limitations, such as limited band selections or conservative band ratio-derived water indices, relying on either manual digitization or simple thresholding methods, focusing on either artificial or smoothly shaped coasts, and using images acquired at considerably different tidal height levels. Therefore, in the present study, a subpixel shoreline extraction approach based on a sustainable cross-generation dataset and a robust edge detection algorithm is proposed. This approach is exemplified by the Zengwun River Estuary located in southwestern Taiwan-Taiwan's most critical coastal preservation region. By quantitatively analyzing the resultant time-series shoreline positions from 1999 to 2021, several hotspots of shoreline recession have been identified: an extreme erosional rate up to -69.4 m year^-1 is revealed in the northern sand bank; while the offshore sand bar demonstrates an overall landward retreat rate of -35.4 ± 1.24 m year^-1.

Effects of age and knee osteoarthritis on the modular control of walking: A pilot study

Background

Older adults and individuals with knee osteoarthritis (KOA) often exhibit reduced locomotor function and altered muscle activity. Identifying age- and KOA-related changes to the modular control of gait may provide insight into the neurological mechanisms underlying reduced walking performance in these populations. The purpose of this pilot study was to determine if the modular control of walking differs between younger and older adults without KOA and adults with end-stage KOA.

Methods

Kinematic, kinetic, and electromyography data were collected from ten younger (23.5 ± 3.1 years) and ten older (63.5 ± 3.4 years) adults without KOA and ten adults with KOA (64.0 ± 4.0 years) walking at their self-selected speed. Separate non-negative matrix factorizations of 500 bootstrapped samples determined the number of modules required to reconstruct each participant’s electromyography. One-way Analysis of Variance tests assessed the effect of group on walking speed and the number of modules. Kendall rank correlations (τ_b) assessed the association between the number of modules and self-selected walking speed.

Results

The number of modules required in the younger adults (3.2 ± 0.4) was greater than in the individuals with KOA (2.3 ± 0.7; p = 0.002), though neither cohorts’ required number of modules differed significantly from the unimpaired older adults (2.7 ± 0.5; p ≥ 0.113). A significant association between module number and walking speed was observed (τ_b = 0.350, p = 0.021) and individuals with KOA walked significantly slower (0.095 ± 0.21 m/s) than younger adults (1.24 ± 0.15 m/s; p = 0.005). Individuals with KOA also exhibited altered module activation patterns and composition (which muscles are associated with each module) compared to unimpaired adults.

Conclusion

These findings suggest aging alone may not significantly alter modular control; however, the combined effects of knee osteoarthritis and aging may together impair the modular control of gait.

Effects of an exoskeleton-assisted gait training on post-stroke lower-limb muscle coordination

Objective.Powered exoskeletons have been used to help persons with gait impairment regain some walking ability. However, little is known about its impact on neuromuscular coordination in persons with stroke. The objective of this study is to investigate how a powered exoskeleton could affect the neuromuscular coordination of persons with post-stroke hemiparesis.Approach.Eleven able-bodied subjects and ten stroke subjects participated in a single-visit treadmill walking assessment, in which their motion and lower-limb muscle activities were captured. By comparing spatiotemporal parameters, kinematics, and muscle synergy pattern between two groups, we characterized the normal gait pattern and the post-stroke motor deficits. Five eligible stroke subjects received exoskeleton-assisted gait trainings and walking assessments were conducted pre-intervention (Pre) and post-intervention (Post), without (WO) and with (WT) the exoskeleton. We compared their gait performance between (a) Pre and Post to investigate the effect of exoskeleton-assisted gait training and, (b) WO and WT the exoskeleton to investigate the effect of exoskeleton wearing on stroke subjects.Main results.While four distinct motor modules were needed to describe lower-extremity activities during stead-speed walking among able-bodied subjects, three modules were sufficient for the paretic leg from the stroke subjects. Muscle coordination complexity, module composition and activation timing were preserved after the training, indicating the intervention did not significantly change the neuromuscular coordination. In contrast, walking WT the exoskeleton altered the stroke subjects' synergy pattern, especially on the paretic side. The changes were dominated by the activation profile modulation towards the normal pattern observed from the able-bodied group.Significance.This study gave us some critical insight into how a powered exoskeleton affects the stroke subjects' neuromuscular coordination during gait and demonstrated the potential to use muscle synergy as a method to evaluate the effect of the exoskeleton training.This study was registered at ClinicalTrials.gov (identifier: NCT03057652).

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network-CNN, Support Vector Machine-SVM, K-Nearest Neighbors-KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: "healthy" and "patient") was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

How to improve the muscle synergy analysis methodology?

Muscle synergy analysis is increasingly used in domains such as neurosciences, robotics, rehabilitation or sport sciences to analyze and better understand motor coordination. The analysis uses dimensionality reduction techniques to identify regularities in spatial, temporal or spatio-temporal patterns of multiple muscle activation. Recent studies have pointed out variability in outcomes associated with the different methodological options available and there was a need to clarify several aspects of the analysis methodology. While synergy analysis appears to be a robust technique, it remain a statistical tool and is, therefore, sensitive to the amount and quality of input data (EMGs). In particular, attention should be paid to EMG amplitude normalization, baseline noise removal or EMG filtering which may diminish or increase the signal-to-noise ratio of the EMG signal and could have major effects on synergy estimates. In order to robustly identify synergies, experiments should be performed so that the groups of muscles that would potentially form a synergy are activated with a sufficient level of activity, ensuring that the synergy subspace is fully explored. The concurrent use of various synergy formulations-spatial, temporal and spatio-temporal synergies- should be encouraged. The number of synergies represents either the dimension of the spatial structure or the number of independent temporal patterns, and we observed that these two aspects are often mixed in the analysis. To select a number, criteria based on noise estimates, reliability of analysis results, or functional outcomes of the synergies provide interesting substitutes to criteria solely based on variance thresholds.

Clinical Relevance of State-of-the-Art Analysis of Surface Electromyography in Cerebral Palsy

Surface electromyography (sEMG) can be used to assess the integrity of the neuromuscular system and its impairment in neurological disorders. Here we will consider several issues related to the current clinical applications, difficulties and limited usage of sEMG for the assessment and rehabilitation of children with cerebral palsy. The uniqueness of this methodology is that it can determine hyperactivity or inactivity of selected muscles, which cannot be assessed by other methods. In addition, it can assist for intervention or muscle/tendon surgery acts, and it can evaluate integrated functioning of the nervous system based on multi-muscle sEMG recordings and assess motor pool activation. The latter aspect is especially important for understanding impairments of the mechanisms of neural controllers rather than malfunction of individual muscles. Although sEMG study is an important tool in both clinical research and neurorehabilitation, the results of a survey on the clinical relevance of sEMG in a typical department of pediatric rehabilitation highlighted its limited clinical usage. We believe that this is due to limited knowledge of the sEMG and its neuromuscular underpinnings by many physiotherapists, as a result of lack of emphasis on this important methodology in the courses taught in physical therapy schools. The lack of reference databases or benchmarking software for sEMG analysis may also contribute to the limited clinical usage. Despite the existence of educational and technical barriers to a widespread use of, sEMG does provide important tools for planning and assessment of rehabilitation treatments for children with cerebral palsy.

Maturation of the Locomotor Circuitry in Children With Cerebral Palsy

The first years of life represent an important phase of maturation of the central nervous system, processing of sensory information, posture control and acquisition of the locomotor function. Cerebral palsy (CP) is the most common group of motor disorders in childhood attributed to disturbances in the fetal or infant brain, frequently resulting in impaired gait. Here we will consider various findings about functional maturation of the locomotor output in early infancy, and how much the dysfunction of gait in children with CP can be related to spinal neuronal networks vs. supraspinal dysfunction. A better knowledge about pattern generation circuitries in infancy may improve our understanding of developmental motor disorders, highlighting the necessity for regulating the functional properties of abnormally developed neuronal locomotor networks as a target for early sensorimotor rehabilitation. Various clinical approaches and advances in biotechnology are also considered that might promote acquisition of the locomotor function in infants at risk for locomotor delays.

Locomotor patterns during obstacle avoidance in children with cerebral palsy

Previous studies mainly evaluated the neuromuscular pattern generation in cerebral palsy (CP) during unobstructed gait. Here we characterized impairments in the obstacle task performance associated with a limited adaptation of the task-relevant muscle module timed to the foot lift during obstacle crossing. Impaired task performance in children with CP may reflect basic developmental deficits in the adaptable control of gait when the locomotor task is superimposed with the voluntary movement.

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within an SO framework improves prediction of muscle activations during walking. A motion/force/electromyography (EMG) gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle–tendon kinematics, and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. Synergy optimization neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation or motion control and prediction.

Cycling Biomechanics and Its Relationship to Performance

State-of-the-art biomechanical laboratories provide a range of tools that allow precise measurements of kinematic, kinetic, motor and physiologic characteristics. Force sensors, motion capture devices and electromyographic recording measure the forces exerted at the pedal, saddle, and handlebar and the joint torques created by muscle activity. These techniques make it possible to obtain a detailed biomechanical analysis of cycling movements. However, despite the reasonable accuracy of such measures, cycling performance remains difficult to fully explain. There is an increasing demand by professionals and amateurs for various biomechanical assessment services. Most of the difficulties in understanding the link between biomechanics and performance arise because of the constraints imposed by the bicycle, human physiology and musculo-skeletal system. Recent studies have also pointed out the importance of evaluating not only output parameters, such as power output, but also intrinsic factors, such as the cyclist coordination. In this narrative review, we present various techniques allowing the assessment of a cyclist at a biomechanical level, together with elements of interpretation, and we show that it is not easy to determine whether a certain technique is optimal or not.

Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running

Muscle synergies provide a simple description of a complex motor control mechanism. Synergies are extracted from muscle activation patterns using factorisation methods. Despite the availability of several factorisation methods in the literature, the most appropriate method for muscle synergy extraction is currently unknown. In this study, we compared four muscle synergy extraction methods: non-negative matrix factorisation, principal component analysis, independent component analysis, and factor analysis. Probability distribution of muscle activation patterns were compared with the probability distribution of synergy excitation primitives obtained from the four factorisation methods. Muscle synergies extracted using non-negative matrix factorisation best matched the probability distribution of muscle activation patterns across different walking and running speeds. Non-negative matrix factorisation also best tracked changes in muscle activation patterns compared to the other factorisation methods. Our results suggest that non-negative matrix factorisation is the best factorisation method for identifying muscle synergies in dynamic tasks with different levels of muscle contraction.

Data-driven spectral analysis for coordinative structures in periodic human locomotion

Living organisms dynamically and flexibly operate a great number of components. As one of such redundant control mechanisms, low-dimensional coordinative structures among multiple components have been investigated. However, structures extracted from the conventional statistical dimensionality reduction methods do not reflect dynamical properties in principle. Here we regard coordinative structures in biological periodic systems with unknown and redundant dynamics as a nonlinear limit-cycle oscillation, and apply a data-driven operator-theoretic spectral analysis, which obtains dynamical properties of coordinative structures such as frequency and phase from the estimated eigenvalues and eigenfunctions of a composition operator. Using segmental angle series during human walking as an example, we first extracted the coordinative structures based on dynamics; e.g. the speed-independent coordinative structures in the harmonics of gait frequency. Second, we discovered the speed-dependent time-evolving behaviours of the phase by estimating the eigenfunctions via our approach on the conventional low-dimensional structures. We also verified our approach using the double pendulum and walking model simulation data. Our results of locomotion analysis suggest that our approach can be useful to analyse biological periodic phenomena from the perspective of nonlinear dynamical systems.

Multiphase and Multivariable Linear Controllers That Account for the Joint Torques in Normal Human Walking

OBJECTIVE

The objective of this paper is to investigate whether a small number of sequentially composed multivariable linear controllers can be used to recover a defining relation between the joint torques, angles, and velocities hidden in the walking data of multiple human subjects.

METHODS

We solve a mixed integer programming problem that defines the optimal multivariable and multiphase relation between the torques, angles, and velocities for the hip, knee, and ankle joints.

RESULTS

Using the data of seven healthy subjects, we show that the aforementioned relation can be remarkably well represented by four sequentially composed and independently activated multivariable linear controllers; the controllers account for [Formula: see text] (mean ± sem) of the variance in the joint torques across subjects, and [Formula: see text] of the variance for a new subject. We further show that each controller is associated with one of the four phases of the gait cycle, separated by toe-off and heel-strike.

CONCLUSION

The proposed controller generalizes previously developed multiphase single variable, and single phase multivariable controllers, to a multiphase multivariable controller that better explains the walking data of multiple subjects, and better generalizes to new subjects.

SIGNIFICANCE

Our result provides strong support to extend previously developed decoupled single joint controllers to coupled multijoint multivariable controllers for the control of human assistive and augmentation devices.

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.

How are Muscle Synergies Affected by Electromyography Pre-Processing?

Muscle synergies have been used for decades to explain a variety of motor behaviors, both in humans and animals and, more recently, to steer rehabilitation strategies. However, many sources of variability such as factorization algorithms, criteria for dimensionality reduction and data pre-processing constitute a major obstacle to the successful comparison of the results obtained by different research groups. Starting from the canonical EMG processing we determined how variations in filter cut-off frequencies and normalization methods, commonly found in literature, affect synergy weights and inter-subject similarity (ISS) using experimental data related to a 15-muscles upper-limb reaching task. Synergy weights were not significantly altered by either normalization (maximum voluntary contraction - MVC - or maximum amplitude of the signal - SELF) or band-pass filter ([20-500 Hz] or [50-500] Hz). Normalization did, however, alter the amount of variance explained by a set of synergies, which is a criterion often used for model order selection. Comparing different low-pass (LP) filters (0.5 Hz, 4 Hz, 10 Hz, 20 Hz cut-offs) we showed that increasing the low pass filter cut-off had the effect of decreasing the variance accounted for by a set number of synergies and affected individual muscle contributions. Extreme smoothing (i.e., LP cut-off 0.5 Hz) enhanced the contrast between active and inactive muscles but had an unpredictable effect on the ISS. The results presented here constitute a further step towards a thoughtful EMG pre-processing for the extraction of muscle synergies.

The motor repertoire of older adult fallers may constrain their response to balance perturbations

Older adults are at a high risk of falls, and most falls occur during locomotor activities like walking. This study aimed to improve our understanding of changes in neuromuscular control associated with increased risk of falls in older adults in the presence of dynamic balance challenges during walking. Motor module (also known as muscle synergy) analyses identified changes in the neuromuscular recruitment of leg muscles during walking with and without perturbations designed to elicit the visual perception of lateral instability. During normal walking we found that a history of falls (but not age) was associated with reduced motor module complexity and that age (but not a history of falls) was associated with increased step-to-step variability of module recruitment timing. Furthermore, motor module complexity was unaltered in the presence of optical flow perturbations. The specific effects of a history of falls on leg muscle recruitment included an absence and/or inability to independently recruit motor modules normally recruited to perform biomechanical functions important for walking balance control. These results suggest that fallers do not recruit the appropriate motor modules necessary for well-coordinated walking balance control even in the presence of perturbations. The identified changes in the modular control of walking balance in older fallers may either represent a neural deficit that leads to poor balance control or a prior history of falls that results in a compensatory motor adaptation. In either case, our study provides initial evidence that a reduced motor repertoire in older adult fallers may be a constraint on their ability to appropriately respond to balance challenges during walking. NEW & NOTEWORTHY This is the first study to demonstrate a reduced motor repertoire during walking in older adults with a history of falls but without any overt neurological deficits. Furthermore, using virtual reality during walking to elicit the visual perception of lateral instability, we provide initial evidence that a reduced motor repertoire in older adult fallers may be a constraint on their ability to appropriately respond to balance challenges during walking.

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.

Speed-Dependent Modulation of Muscle Activity Based on Muscle Synergies during Treadmill Walking

The regulation of walking speed is easily achieved. However, the central nervous system (CNS) must coordinate numerous muscles in order to achieve a smooth and continuous control of walking speed. To control walking speed appropriately, the CNS may need to utilize a simplified system for the control of numerous muscles. Previous studies have revealed that the CNS may control walking via muscle synergies that simplify the control of muscles by modularly organizing several muscles. We hypothesized that the CNS controls the walking speed by flexibly modulating activation of muscle synergies within one gait cycle. Then, we investigated how the activation of muscle synergies depend on walking speeds using the center of activity (CoA) that indicates the center of the distribution of activation timing within one gait cycle. Ten healthy men walked on a treadmill at 14 different walking speeds. We measured the surface electromyograms (EMGs) and kinematic data. Muscle synergies were extracted using non-negative matrix factorization. Then, we calculated the CoA of each muscle synergy. We observed that the CoA of each specific synergy would shift as the walking speed changed. The CoA that was mainly activated during the heel contact phase (C₁) and the activation that contributed to the double support phase (C₃) shifted to the earlier phase as the walking speed increased, whereas the CoA that produced swing initiation motion (C₄) and the activation that related to the late-swing phase (C₅) shifted to the later phase. This shifting of the CoA indicates that the CNS controls intensive activation of muscle synergies during the regulation of walking speed. In addition, shifting the CoA might be associated with changes in kinematics or kinetics depending on the walking speed. We concluded that the CNS flexibly controls the activation of muscle synergies in regulation of walking speed.

Motor modules during adaptation to walking in a powered ankle exoskeleton

BACKGROUND

Modules of muscle recruitment can be extracted from electromyography (EMG) during motions, such as walking, running, and swimming, to identify key features of muscle coordination. These features may provide insight into gait adaptation as a result of powered assistance. The aim of this study was to investigate the changes (module size, module timing and weighting patterns) of surface EMG data during assisted and unassisted walking in an powered, myoelectric, ankle-foot orthosis (ankle exoskeleton).

METHODS

Eight healthy subjects wore bilateral ankle exoskeletons and walked at 1.2 m/s on a treadmill. In three training sessions, subjects walked for 40 min in two conditions: unpowered (10 min) and powered (30 min). During each session, we extracted modules of muscle recruitment via nonnegative matrix factorization (NNMF) from the surface EMG signals of ten muscles in the lower limb. We evaluated reconstruction quality for each muscle individually using R2 and normalized root mean squared error (NRMSE). We hypothesized that the number of modules needed to reconstruct muscle data would be the same between conditions and that there would be greater similarity in module timings than weightings.

RESULTS

Across subjects, we found that six modules were sufficient to reconstruct the muscle data for both conditions, suggesting that the number of modules was preserved. The similarity of module timings and weightings between conditions was greater then random chance, indicating that muscle coordination was also preserved. Motor adaptation during walking in the exoskeleton was dominated by changes in the module timings rather than module weightings. The segment number and the session number were significant fixed effects in a linear mixed-effect model for the increase in R2 with time.

CONCLUSIONS

Our results show that subjects walking in a exoskeleton preserved the number of modules and the coordination of muscles within the modules across conditions. Training (motor adaptation within the session and motor skill consolidation across sessions) led to improved consistency of the muscle patterns. Subjects adapted primarily by changing the timing of their muscle patterns rather than the weightings of muscles in the modules. The results of this study give new insight into strategies for muscle recruitment during adaptation to a powered ankle exoskeleton.

Similarity of muscle synergies extracted from the lower limb including the deep muscles between level and uphill treadmill walking

This study aimed to examine muscle synergies involving the deeper muscles of the lower limb during level and uphill treadmill walking. Seven men and five women walked on a treadmill at three speeds (60, 80, and 100m/min) and two grades (level and 10% grade). Surface electromyographic (EMG) signals were recorded from 10 muscles of the lower limb, including vastus intermedius, adductor magnus, and adductor longus. Muscle synergies were extracted applying non-negative matrix factorization, and the relative co-activation across muscles and the temporal information of synergy recruitment were identified by the muscle synergy vector and synergy activation coefficient, respectively. Correlation coefficients between a pair of synergy vectors during level and uphill walking were analyzed as a similarity index, with the similarity criterion at r=0.76. Changes in synergy activation coefficients between the walking conditions were evaluated by cross-correlation analysis. The mean number of synergies ranged from 3.8 to 4.0 across all conditions, and they were not significantly different between level and uphill walking conditions. Similarity between walking conditions was high (r>0.76) for three muscle synergies, but not for one synergy that mainly consisted of the quadriceps femoris. The inter-condition similarity of the synergy activation coefficients was high for the four synergies, and a significant lag time for synergy 2, which consisted mainly of the activity of medial gastrocnemius, was found at 60 and 80m/min. The muscle synergies extracted from the lower limb involving the deeper muscles appear to be consistent during level and uphill treadmill walking.

Immature Spinal Locomotor Output in Children with Cerebral Palsy

Detailed descriptions of gait impairments have been reported in cerebral palsy (CP), but it is still unclear how maturation of the spinal motoneuron output is affected. Spatiotemporal alpha-motoneuron activation during walking can be assessed by mapping the electromyographic activity profiles from several, simultaneously recorded muscles onto the anatomical rostrocaudal location of the motoneuron pools in the spinal cord, and by means of factor analysis of the muscle activity profiles. Here, we analyzed gait kinematics and EMG activity of 11 pairs of bilateral muscles with lumbosacral innervation in 35 children with CP (19 diplegic, 16 hemiplegic, 2-12 years) and 33 typically developing (TD) children (1-12 years). TD children showed a progressive reduction of EMG burst durations and a gradual reorganization of the spatiotemporal motoneuron output with increasing age. By contrast, children with CP showed very limited age-related changes of EMG durations and motoneuron output, as well as of limb intersegmental coordination and foot trajectory control (on both sides for diplegic children and the affected side for hemiplegic children). Factorization of the EMG signals revealed a comparable structure of the motor output in children with CP and TD children, but significantly wider temporal activation patterns in children with CP, resembling the patterns of much younger TD infants. A similar picture emerged when considering the spatiotemporal maps of alpha-motoneuron activation. Overall, the results are consistent with the idea that early injuries to developing motor regions of the brain substantially affect the maturation of the spinal locomotor output and consequently the future locomotor behavior.

Synergistic Structure in the Speed Dependent Modulation of Muscle Activity in Human Walking

Recently, a modular organisation has been proposed to simplify control of the large number of muscles involved in human walking. Although previous research indicates that a single set of modular activation patterns can account for muscle activity at different speeds, these studies only provide indirect evidence for the idea that speed regulation in human walking is under modular control. Here, a more direct approach was taken to assess the synergistic structure that underlies speed regulation, by isolating speed effects through the construction of gain functions that represent the linear relation between speed and amplitude for each point in the time-normalized gait cycle. The activity of 13 muscles in 13 participants was measured at 4 speeds (0.69, 1.00, 1.31, and 1.61 ms^-1) during treadmill walking. Gain functions were constructed for each of the muscles, and gain functions and the activity patterns at 1.00 ms^-1 were both subjected to dimensionality reduction, to obtain modular gain functions and modular basis functions, respectively. The results showed that 4 components captured most of the variance in the gain functions (74.0% ± 1.3%), suggesting that the neuromuscular regulation of speed is under modular control. Correlations between modular gain functions and modular basis functions (range 0.58–0.89) and the associated synergistic muscle weightings (range 0.6–0.95) were generally high, suggesting substantial overlap in the synergistic control of the basic phasing of muscle activity and its modulation through speed. Finally, the combined set of modular functions and associated weightings were well capable of predicting muscle activity patterns obtained at a speed (1.31 ms^-1) that was not involved in the initial dimensionality reduction, confirming the robustness of the presently used approach. Taken together, these findings provide direct evidence of synergistic structure in speed regulation, and may inspire further work on flexibility in the modular control of gait.

Synergy temporal sequences and topography in the spinal cord: evidence for a traveling wave in frog locomotion

Locomotion is produced by a central pattern generator. Its spinal cord organization is generally considered to be distributed, with more rhythmogenic rostral lumbar segments. While this produces a rostrocaudally traveling wave in undulating species, this is not thought to occur in limbed vertebrates, with the exception of the interneuronal traveling wave demonstrated in fictive cat scratching (Cuellar et al. J Neurosci 29:798-810, 2009). Here, we reexamine this hypothesis in the frog, using the seven muscle synergies A to G previously identified with intraspinal NMDA (Saltiel et al. J Neurophysiol 85:605-619, 2001). We find that locomotion consists of a sequence of synergy activations (A-B-G-A-F-E-G). The same sequence is observed when focal NMDA iontophoresis in the spinal cord elicits a caudal extension-lateral force-flexion cycle (flexion onset without the C synergy). Examining the early NMDA-evoked motor output at 110 sites reveals a rostrocaudal topographic organization of synergy encoding by the lumbar cord. Each synergy is preferentially activated from distinct regions, which may be multiple, and partially overlap between different synergies. Comparing the sequence of synergy activation in locomotion with their spinal cord topography suggests that the locomotor output is achieved by a rostrocaudally traveling wave of activation in the swing-stance cycle. A two-layer circuitry model, based on this topography and a traveling wave reproduces this output and explores its possible modifications under different afferent inputs. Our results and simulations suggest that a rostrocaudally traveling wave of excitation takes advantage of the topography of interneuronal regions encoding synergies, to activate them in the proper sequence for locomotion.

Neuromuscular adjustments of gait associated with unstable conditions

A compact description of coordinated muscle activity is provided by the factorization of electromyographic (EMG) signals. With the use of this approach, it has consistently been shown that multimuscle activity during human locomotion can be accounted for by four to five modules, each one comprised of a basic pattern timed at a different phase of gait cycle and the weighting coefficients of synergistic muscle activations. These modules are flexible, in so far as the timing of patterns and the amplitude of weightings can change as a function of gait speed and mode. Here we consider the adjustments of the locomotor modules related to unstable walking conditions. We compared three different conditions, i.e., locomotion of healthy subjects on slippery ground (SL) and on narrow beam (NB) and of cerebellar ataxic (CA) patients on normal ground. Motor modules were computed from the EMG signals of 12 muscles of the right lower limb using non-negative matrix factorization. The unstable gait of SL, NB, and CA showed significant changes compared with controls in the stride length, stride width, range of angular motion, and trunk oscillations. In most subjects of all three unstable conditions, >70% of the overall variation of EMG waveforms was accounted for by four modules that were characterized by a widening of muscle activity patterns. This suggests that the nervous system adopts the strategy of prolonging the duration of basic muscle activity patterns to cope with unstable conditions resulting from either slippery ground, reduced support surface, or pathology.

Muscle synergies and spinal maps are sensitive to the asymmetry induced by a unilateral stroke

Background

Previous studies have shown that a cerebrovascular accident disrupts the coordinated control of leg muscles during locomotion inducing asymmetric gait patterns. However, the ability of muscle synergies and spinal maps to reflect the redistribution of the workload between legs after the trauma has not been investigated so far.

Methods

To investigate this issue, twelve post-stroke and ten healthy participants were asked to walk on a treadmill at controlled speeds (0.5, 0.7, 0.9, 1.1 km/h), while the EMG activity of twelve leg muscles was recorded on both legs. The synergies underlying muscle activation and the estimated motoneuronal activity in the lumbosacral enlargement (L2-S2) were computed and compared between groups.

Results

Results showed that muscle synergies in the unaffected limb were significantly more comparable to those of the healthy control group than the ones in the affected side. Spinal maps were dissimilar between the affected and unaffected sides highlighting a significant shift of the foci of the activity toward the upper levels of the spinal cord in the unaffected leg.

Conclusions

Muscle synergies and spinal maps reflect the asymmetry as a motor deficit after stroke. However, further investigations are required to support or reject the hypothesis that the altered muscular organization highlighted by muscle synergies and spinal maps may be due to the concomitant contribution of the altered information coming from the upper part of the CNS, as resulting from the stroke, and to the abnormal sensory feedback due to the neuromuscular adaptation of the patients.

Validating the feedback control of intersegmental coordination by fluctuation analysis of disturbed walking

A walking motion is established by feedforward control for rhythmic locomotion and feedback control for adapting to environmental variations. To identify the control variables that underlie feedback control, uncontrolled manifold (UCM) analysis has been proposed and adopted for analyzing various movements. UCM analysis searches the controlled variables by comparing the fluctuation size of segmental groups related and unrelated to the movement of candidate variables, based on the assumption that a small fluctuation size indicates a relationship with the feedback control. The present study was based on UCM analysis and evaluated fluctuation size to determine the control mechanism for walking. While walking, the subjects were subjected to floor disturbances at two different frequencies, and the fluctuation sizes of the segmental groups related to characteristic variables were calculated and compared. The characteristic variables evaluated were the motion of the center of mass, limb axis, and head, and the intersegmental coordination of segmental groups with simultaneous coupled movements. Results showed that the fluctuations in intersegmental coordination were almost equally small for any segment, while fluctuations in the other variables were large in certain segments. Moreover, a comparison of the fluctuation sizes among the evaluated variables showed that the fluctuation size for intersegmental coordination was the smallest. These results indicate a possible relationship between intersegmental coordination and the control of walking.

Selecting the best number of synergies in gait: preliminary results on young and elderly people

Matrix factorization algorithms are increasingly used to extract meaningful information from multivariate EMG datasets. However a key issue is the selection of the number of synergies (i.e., model order) to retain. In this preliminary work a set of criteria, based on Independent Component Analysis, was developed to determine the number of synergies to extract from a multivariate EMG dataset, and applied on EMG signals acquired from 12 leg muscles during walking at different cadences (40, 60, …, 140 strides per minute) in young and elderly subjects. The method was tested on ad-hoc created datasets with a predetermined number of embedded sources and amplitude of added noise. Young subjects walking patterns are explained by a number of synergies not significantly different with respect to elderly subjects. The inter-subject variability is greater at high (elderly) and low (young and elderly) cadences suggesting that the walking pattern is more stable at central frequencies. The type of preprocessing influences the number of underlying synergies: an increased number of independent components is needed to explain the variability of unfiltered data. The proposed method could serve as a guideline to scientists in the evaluation of walking performance. Further developments will include a validation of the method and its extension to other factorization algorithms.

Coordination among thigh muscles including the vastus intermedius and adductor magnus at different cycling intensities

Although many studies have been focused on muscle synergies in the lower limbs, synergies of the thigh muscles during cycling have not been investigated in detail. We examined synergies of the thigh muscles including the vastus intermedius (VI) and adductor magnus (AM) while cycling. Eight healthy men pedaled at 20%, 40%, 60%, 80% and 100% of maximal aerobic power output at a constant cadence of 60 rpm. Surface electromyography (EMG) recorded signals from the deep VI and the three superficial quadriceps femoris (QF) muscles, the two hamstrings and the AM. The root mean square of the EMG signal was averaged every 2° of crank rotation and normalized by the peak value for each muscle. We used factor analysis to assess normalized EMG recordings while cycling and to identify thigh muscle synergies. The VI, the superficial QF muscles and the AM dominated the first muscle synergy at all power output levels. The AM also formed a second synergy with the two hamstrings at all power output levels. These results suggest that the VI coordinates with the other QF and AM muscles, and that the AM coordinates with the QF and hamstring muscles while cycling.

Task constraints and minimization of muscle effort result in a small number of muscle synergies during gait

Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a fixed ratio by a single input signal. Because of the reduced number of input signals, synergy-based control is low dimensional. But a major criticism on the theory of synergy-based control of muscles is that muscle synergies might reflect task constraints rather than a neural control strategy. Another theory of motor control suggests that muscles are recruited by optimizing performance. Optimization of performance has been widely used to calculate muscle recruitment underlying a given motion while assuming independent recruitment of muscles. If synergies indeed determine muscle recruitment underlying a given motion, optimization approaches that do not model synergy-based control could result in muscle activations that do not show the synergistic muscle action observed through electromyography (EMG). If, however, synergistic muscle action results from performance optimization and task constraints (joint kinematics and external forces), such optimization approaches are expected to result in low-dimensional synergistic muscle activations that are similar to EMG-based synergies. We calculated muscle recruitment underlying experimentally measured gait patterns by optimizing performance assuming independent recruitment of muscles. We found that the muscle activations calculated without any reference to synergies can be accurately explained by on average four synergies. These synergies are similar to EMG-based synergies. We therefore conclude that task constraints and performance optimization explain synergistic muscle recruitment from a redundant set of muscles.

A review on the coordinative structure of human walking and the application of principal component analysis

Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.

Muscle Synergies During a Single-Leg Drop-Landing in Boys and Girls

The purpose of this study was to investigate muscle activation patterns during a landing task in boys and girls through the use of muscle synergies. Electromyographical data from six lower extremity muscles were collected from 11 boys and 16 girls while they performed single-leg drop-landings. Electromyographical data from six leg muscles were rectified, smoothed, and normalized to maximum dynamic muscle activity during landing. Data from 100 ms before to 100 ms after touchdown were submitted to factor analyses to extract muscle synergies along with the associated activation and weighing coefficients. Boys and girls both used three muscle synergies. The activation coefficients of these synergies captured muscle activity during the prelanding, touchdown, and postlanding phases of the single-leg drop-landing. Analysis of the weighing coefficients indicated that within the extracted muscle synergies the girls emphasized activation of the medial hamstring muscle during the prelanding and touchdown synergy whereas boys emphasized activation of the vastus medialis during the postlanding synergy. Although boys and girls use similar muscle synergies during single-leg drop-landings, they differed in which muscles were emphasized within these synergies. The observed differences in aspects related to the muscle synergies during landing may have implications with respect to knee injury risk.

Principal Component Analysis of gait in Parkinson's disease: relevance of gait velocity

Principal Component Analysis (PCA) is a method to estimate the relation between data points. We used PCA to analyse movements of the upper and lower extremities during treadmill walking in healthy subjects and two groups of Parkinsonian patients. Healthy subjects (n=35) showed a typical pattern with high values of PC1 and low values in a descending order of PC2-PC4. Increase of speed resulted in a significant increase of PC1 and a significant decrease of the following PC's. In more severely affected patients (n=19, UPDRS>20), PC1 was significantly decreased and PC2-PC4 were significantly increased compared to healthy subjects. Speed could be increased only within a small range without corresponding changes of the PC's. In less severely affected patients (n=17), significant differences of the PC's were only found with fast pace. Separate analysis of arms and legs revealed that these changes are only due to altered movements of the arm. Analysis of the pattern of PC's in response to changes of gait velocities reveal alterations even in less severely affected Parkinsonian patients. The changes of the PC's with higher gait velocities in healthy subjects are suggestive of an increase of intersegmental coordination. This is impaired even in less severely affected Parkinsonian patients.

Muscle activation patterns are bilaterally linked during split-belt treadmill walking in humans

There is growing evidence that human locomotion is controlled by flexibly combining a set of basic muscle activity patterns. To explore how these patterns are modified to cope with environmental constraints, 10 healthy young adults 1st walked on a split-belt treadmill at symmetric speeds of 4 and 6 km/h for 2 min. An asymmetric condition was then performed for 10 min in which treadmill speeds for the dominant (fast) and nondominant (slow) sides were 6 and 4 km/h, respectively. This was immediately followed by a symmetric speed condition of 4 km/h for 5 min. Gait kinematics and ground reaction forces were recorded. Electromyography (EMG) was collected from 12 lower limb muscles on each side of the body. Nonnegative matrix factorization was applied to the EMG signals bilaterally and unilaterally to obtain basic activation patterns. A cross-correlation analysis was then used to quantify temporal changes in the activation patterns. During the early (1st 10 strides) and late (final 10 strides) phases of the asymmetric condition, the patterns related to ankle plantar flexor (push-off) of the fast limb and quadriceps muscle (contralateral heel contact) of the slow limb occurred earlier in the gait cycle compared with the symmetric conditions. Moreover, a bilateral temporal alignment of basic patterns between limbs was still maintained in the split-belt condition since a similar shift was observed in the unilateral patterns. The results suggest that the temporal structure of these locomotor patterns is shaped by sensory feedback and that the patterns are bilaterally linked.

Effects of early and intensive neuro-rehabilitative treatment on muscle synergies in acute post-stroke patients: a pilot study

BACKGROUND

After a stroke, patients show significant modifications of neural control of movement, such as abnormal muscle co-activation, and reduced selectivity and modulation of muscle activity. Nonetheless, results reported in literature do not allow to unequivocally explain whether and, in case, how a cerebrovascular accident affects muscle synergies underlying the control of the upper limb. These discrepancies suggest that a complete understanding of the modular re-organization of muscle activity due to a stroke is still lacking. This pilot study aimed at investigating the effects of the conjunction between the natural ongoing of the pathology and the intense robot-mediated treatment on muscle synergies of the paretic upper limb of subacute post-stroke patients.

METHODS

Six subacute patients, homogenous with respect to the age and the time elapsed from the trauma, and ten healthy age-matched subjects were enrolled. The protocol consisted in achieving planar movement of the upper limb while handling the end-effector of a robotic platform. Patients underwent 6 weeks long treatment while clinical scores, kinematics of the end-effector and muscle activity were recorded. Then we verified whether muscle coordination underlying the motor task was significantly affected by the cerebrovascular accident and how muscle synergies were modified along the treatment.

RESULTS

Results show that although muscle synergies in subacute stroke patients were qualitatively comparable to those of healthy subjects, those underlying the movement of the shoulder can reflect the functional deficit induced by the pathology. Moreover, the improvement of motor performance due to the treatment was achieved in conjunction with slight modifications of muscle synergies. In this regard, modifications of muscle synergies appeared to be influenced by the different recovering mechanisms across patients presumably due to the heterogeneity of lesions, sides and location of the accident.

CONCLUSIONS

The results support the hypothesis that muscle synergies reflect the injury of the cerebrovascular accident and could document the effects of the functional recovery due to a suitable and customized treatment. Therefore, they open up new possibilities for the development of more effective neuro-rehabilitation protocols.

Plasticity and modular control of locomotor patterns in neurological disorders with motor deficits

Human locomotor movements exhibit considerable variability and are highly complex in terms of both neural activation and biomechanical output. The building blocks with which the central nervous system constructs these motor patterns can be preserved in patients with various sensory-motor disorders. In particular, several studies highlighted a modular burst-like organization of the muscle activity. Here we review and discuss this issue with a particular emphasis on the various examples of adaptation of locomotor patterns in patients (with large fiber neuropathy, amputees, stroke and spinal cord injury). The results highlight plasticity and different solutions to reorganize muscle patterns in both peripheral and central nervous system lesions. The findings are discussed in a general context of compensatory gait mechanisms, spatiotemporal architecture and modularity of the locomotor program.

A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives

Human locomotion has been described as being generated by an impulsive (burst-like) excitation of groups of musculotendon units, with timing dependent on the biomechanical goal of the task. Despite this view being supported by many experimental observations on specific locomotion tasks, it is still unknown if the same impulsive controller (i.e., a low-dimensional set of time-delayed excitastion primitives) can be used as input drive for large musculoskeletal models across different human locomotion tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle electromyograms in two healthy subjects during four motor tasks. These included walking, running, sidestepping, and crossover cutting maneuvers. The extracted non-negative factors were then averaged and parameterized to obtain task-generic Gaussian-shaped impulsive excitation curves or primitives. These were used to drive a subject-specific musculoskeletal model of the human lower extremity. Results showed that the same set of five impulsive excitation primitives could be used to predict the dynamics of 34 musculotendon units and the resulting hip, knee and ankle joint moments (i.e., NRMSE = 0.18 ± 0.08, and R² = 0.73 ± 0.22 across all tasks and subjects) without substantial loss of accuracy with respect to using experimental electromyograms (i.e., NRMSE = 0.16 ± 0.07, and R² = 0.78 ± 0.18 across all tasks and subjects). Results support the hypothesis that biomechanically different motor tasks might share similar neuromuscular control strategies. This might have implications in neurorehabilitation technologies such as human-machine interfaces for the torque-driven, proportional control of powered prostheses and orthoses. In this, device control commands (i.e., predicted joint torque) could be derived without direct experimental data but relying on simple parameterized Gaussian-shaped curves, thus decreasing the input drive complexity and the number of needed sensors.

Three-dimensional modular control of human walking

Recent studies have suggested that complex muscle activity during walking may be controlled using a reduced neural control strategy organized around the co-excitation of multiple muscles, or modules. Previous computer simulation studies have shown that five modules satisfy the sagittal-plane biomechanical sub-tasks of 2D walking. The present study shows that a sixth module, which contributes primarily to mediolateral balance control and contralateral leg swing, is needed to satisfy the additional non-sagittal plane demands of 3D walking. Body support was provided by Module 1 (hip and knee extensors, hip abductors) in early stance and Module 2 (plantarflexors) in late stance. In early stance, forward propulsion was provided by Module 4 (hamstrings), but net braking occurred due to Modules 1 and 2. Forward propulsion was provided by Module 2 in late stance. Module 1 accelerated the body medially throughout stance, dominating the lateral acceleration in early stance provided by Modules 4 and 6 (adductor magnus) and in late stance by Module 2, except near toe-off. Modules 3 (ankle dorsiflexors, rectus femoris) and 5 (hip flexors and adductors except adductor magnus) accelerated the ipsilateral leg forward in early swing whereas Module 4 decelerated the ipsilateral leg prior to heel-strike. Finally, Modules 1, 4 and 6 accelerated the contralateral leg forward prior to and during contralateral swing. Since the modules were based on experimentally measured muscle activity, these results provide further evidence that a simple neural control strategy involving muscle activation modules organized around task-specific biomechanical functions may be used to control complex human movements.

Patterned control of human locomotion

There is much experimental evidence for the existence of biomechanical constraints which simplify the problem of control of multi-segment movements. In addition, it has been hypothesized that movements are controlled using a small set of basic temporal components or activation patterns, shared by several different muscles and reflecting global kinematic and kinetic goals. Here we review recent studies on human locomotion showing that muscle activity is accounted for by a combination of few basic patterns, each one timed at a different phase of the gait cycle. Similar patterns are involved in walking and running at different speeds, walking forwards or backwards, and walking under different loading conditions. The corresponding weights of distribution to different muscles may change as a function of the condition, allowing highly flexible control. Biomechanical correlates of each activation pattern have been described, leading to the hypothesis that the co-ordination of limb and body segments arises from the coupling of neural oscillators between each other and with limb mechanical oscillators. Muscle activations need only intervene during limited time epochs to force intrinsic oscillations of the system when energy is lost.

Principal component analysis of gait kinematics data in acute and chronic stroke patients

We present the joint angles analysis by means of the principal component analysis (PCA). The data from twenty-seven acute and chronic hemiplegic patients were used and compared with data from five healthy subjects. The data were collected during walking along a 10-meter long path. The PCA was applied on a data set consisting of hip, knee, and ankle joint angles of the paretic and the nonparetic leg. The results point to significant differences in joint synergies between the acute and chronic hemiplegic patients that are not revealed when applying typical methods for gait assessment (clinical scores, gait speed, and gait symmetry). The results suggest that the PCA allows classification of the origin for the deficit in the gait when compared to healthy subjects; hence, the most appropriate treatment can be applied in the rehabilitation.

Age-related modifications of muscle synergies and spinal cord activity during locomotion

Recent findings have shown that neural circuits located in the spinal cord drive muscular activations during locomotion while intermediating between descending signals and peripheral sensory information. This relationship could be modified by the natural aging process. To address this issue, the activity of 12 ipsilateral leg muscles was analyzed in young and elderly people (7 subjects per group) while walking at six different cadences (40-140 steps/min). These signals were used to extract synergies underlying muscle activation and to map the motoneuronal activity of the pools belonging to the lumbosacral enlargement (L(2)-S(2)). The comparison between the two groups showed that neither temporal patterning of motor primitives nor muscles loading synergies seemed to be significantly affected by aging. Conversely, as the cadence increased, spinal maps differ significantly between the groups, showing higher and scattered activity during the whole gait cycle in elders and well-defined bursts in young subjects. The results suggested that motor primitives lead the synchronization of muscle activation mainly depending on the biomechanical demand of the locomotion; hence they are not significantly affected by aging. Nevertheless, at the spinal cord level, biomechanical requirements, peripheral afference, and descending inputs are differently integrated between the two groups, probably reflecting age-related changes of both nervous system and motor control strategies during locomotion.

Patterns of muscle coordination vary with stride frequency during weight assisted treadmill walking

Partial body weight-supported treadmill training is an approach for gait rehabilitation. Variables such as stepping frequency and the amount of body weight support are key parameters manipulated during training. The purpose of this study was to quantify the extent to which body weight support and stride frequency contribute and interact to produce the coordination patterns of the leg muscles. Principal components analysis was used to provide insight into the interaction effects of these factors on electromyographical (EMG) activity during treadmill locomotion. Eight healthy subjects walked on a treadmill at 15 different combinations of weight support (0%, 20%, 40%, 60%, 100%), and stride frequency (0.40, 0.49, 0.57 Hz). Treadmill walking was performed with the Lokomat robotic gait orthosis to constrain leg kinematics. Surface EMG data were collected from several lower limb muscles. Results indicate that much of the variance in EMG activity during treadmill locomotion can be attributed to the mechanics of the locomotor task imposed by the level of body weight support and stride frequency. We also showed that body weight support and stride frequency interact in different ways to affect muscle coordination patterns. EMG coordination patterns are similar between conditions of high levels of body weight support and faster stride frequencies vs. lower levels of body weight support and slower stride frequency. Our data suggest that the interaction of body weight support and stride frequency should be taken into consideration for optimizing motor output during locomotor training.

Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke

Evidence suggests that the nervous system controls motor tasks using a low-dimensional modular organization of muscle activation. However, it is not clear if such an organization applies to coordination of human walking, nor how nervous system injury may alter the organization of motor modules and their biomechanical outputs. We first tested the hypothesis that muscle activation patterns during walking are produced through the variable activation of a small set of motor modules. In 20 healthy control subjects, EMG signals from eight leg muscles were measured across a range of walking speeds. Four motor modules identified through nonnegative matrix factorization were sufficient to account for variability of muscle activation from step to step and across speeds. Next, consistent with the clinical notion of abnormal limb flexion-extension synergies post-stroke, we tested the hypothesis that subjects with post-stroke hemiparesis would have altered motor modules, leading to impaired walking performance. In post-stroke subjects (n = 55), a less complex coordination pattern was shown. Fewer modules were needed to account for muscle activation during walking at preferred speed compared with controls. Fewer modules resulted from merging of the modules observed in healthy controls, suggesting reduced independence of neural control signals. The number of modules was correlated to preferred walking speed, speed modulation, step length asymmetry, and propulsive asymmetry. Our results suggest a common modular organization of muscle coordination underlying walking in both healthy and post-stroke subjects. Identification of motor modules may lead to new insight into impaired locomotor coordination and the underlying neural systems.

Modular control of human walking: Adaptations to altered mechanical demands

Studies have suggested that the nervous system may adopt a control scheme in which synergistic muscle groups are controlled by common excitation patters, or modules, to simplify the coordination of movement tasks such as walking. A recent computer modeling and simulation study of human walking using experimentally derived modules as the control inputs provided evidence that individual modules are associated with specific biomechanical subtasks, such as generating body support and forward propulsion. The present study tests whether the modules identified during normal walking could produce simulations of walking when the mechanical demands were substantially altered. Walking simulations were generated that emulated human subjects who had their body weight and/or body mass increased and decreased by 25%. By scaling the magnitude of five module patterns, the simulations could emulate the subjects' response to each condition by simply scaling the mechanical output from modules associated with specific biomechanical subtasks. Specifically, the modules associated with providing body support increased (decreased) their contribution to the vertical ground reaction force when body weight was increased (decreased) and the module associated with providing forward propulsion increased its contribution to the positive anterior-posterior ground reaction force and positive trunk power when the body mass was increased. The modules that contribute to controlling leg swing were unaffected by the perturbations. These results support the idea that the nervous system may use a modular control strategy and that flexible modulation of module recruitment intensity may be sufficient to meet large changes in mechanical demand.

Muscle synergies during walking: comparison between young and elderly people. Preliminary results

The present work was aimed at analysing the young and elderly people muscular activation in order to provide evidences regarding the different role of the muscular groups in locomotion. The tested hypothesis concerns the different strategy of the counterparts, that is a greater effort in preserving stability and safety in elders versus a greater importance in the propulsion in young. Five young and five elderly subjects were involved in the experiments and were asked to walk with 6 cadences, from 40 to 140 step/min. Muscle activations of twelve muscles were recorded, filtered, time-interpolated in 200 points over a gait cycle, averaged on the steps and processed by using Factor Analysis. Results showed that young and elderly muscle activations were described by factors behaving in opposite manner between the groups. The authors hypothesized that ageing may involve modifications in the peripheral areas of the Central Nervous System, producing neuromuscular adaptation.