Methodological Choices in Muscle Synergy Analysis Impact Differentiation of Physiological Characteristics Following Stroke

Muscle synergies are shared across fundamental subtasks in complex movements of skateboarding

A common theory of motor control posits that movement is controlled by muscle synergies. However, the behavior of these synergies during highly complex movements remains largely unexplored. Skateboarding is a hardly researched sport that requires rapid motor control to perform tricks. The objectives of this study were to investigate three key areas: (i) whether motor complexity differs between skateboard tricks, (ii) the inter-participant variability in synergies, and (iii) whether synergies are shared between different tricks. Electromyography data from eight muscles per leg were collected from seven experienced skateboarders performing three different tricks (Ollie, Kickflip, 360°-flip). Synergies were extracted using non-negative matrix factorization. The number of synergies (NoS) was determined using two criteria based on the total variance accounted for (tVAF > 90% and adding an additional synergy does not increase tVAF > 1%). In summary: (i) NoS and tVAF did not significantly differ between tricks, indicating similar motor complexity. (ii) High inter-participant variability exists across participants, potentially caused by the low number of constraints given to perform the tricks. (iii) Shared synergies were observed in every comparison of two tricks. Furthermore, each participant exhibited at least one synergy vector, which corresponds to the fundamental 'jumping' task, that was shared through all three tricks.

Changes in walking function and neural control following pelvic cancer surgery with reconstruction

Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.

Evidence of synergy coordination patterns of upper-limb motor control in stroke patients with mild and moderate impairment

Objectives: Previous studies showed that the central nervous system (CNS) controls movements by recruiting a low-dimensional set of modules, usually referred to as muscle synergies. Stroke alters the structure and recruitment patterns of muscle synergies, leading to abnormal motor performances. Some studies have shown that muscle synergies can be used as biomarkers for assessing motor function. However, coordination patterns of muscle synergies in post-stroke patients need more investigation to characterize how they are modified in functional movements. Methods: Thirteen mild-to-moderate stroke patients and twenty age-matched healthy subjects were recruited to perform two upper-limb movements, hand-to-mouth movement and reaching movement. Muscle synergies were extracted with nonnegative matrix factorization. We identified a set of reference synergies (i.e., averaged across healthy subjects) and typical synergies (i.e., averaged across stroke subjects) from the healthy group and stroke group respectively, and extracted affected synergies from each patient. Synergy similarity between groups was computed and analyzed. Synergy reconstruction analysis was performed to verify synergy coordination patterns in post-stroke patients. Results: On average, three synergies were extracted from both the healthy and stroke groups, while the mild impairment group had a significantly higher number of synergies than the healthy group. The similarity analysis showed that synergy structure was more consistent in the healthy group, and stroke instead altered synergy structure and induced more variability. Synergy reconstruction analysis at group and individual levels showed that muscle synergies of patients often showed a combination of healthy reference synergies in the analyzed movements. Finally, this study associated four synergy coordination patterns with patients: merging (equilibrium and disequilibrium), sharing (equilibrium and disequilibrium), losing, and preservation. The preservation was mainly represented in the mild impairment group, and the moderate impairment group showed more merging and sharing. Conclusion: This study concludes that stroke shows more synergy variability compared to the healthy group and the alterations of muscle synergies can be described as a combination of reference synergies by four synergy coordination patterns. These findings deepen the understanding of the underlying neurophysiological mechanisms and possible motor control strategies adopted by the CNS in post-stroke patients.

Muscle synergies for evaluating upper limb in clinical applications: A systematic review

INTRODUCTION

Muscle synergies have been proposed as a strategy employed by the central nervous system to control movements. Muscle synergy analysis is a well-established framework to examine the pathophysiological basis of neurological diseases and has been applied for analysis and assessment in clinical applications in the last decades, even if it has not yet been widely used in clinical diagnosis, rehabilitative treatment and interventions. Even if inconsistencies in the outputs among studies and lack of a normative pipeline including signal processing and synergy analysis limit the progress, common findings and results are identifiable as a basis for future research. Therefore, a literature review that summarizes methods and main findings of previous works on upper limb muscle synergies in clinical environment is needed to i) summarize the main findings so far, ii) highlight the barriers limiting their use in clinical applications, and iii) suggest future research directions needed for facilitating translation of experimental research to clinical scenarios.

METHODS

Articles in which muscle synergies were used to analyze and assess upper limb function in neurological impairments were reviewed. The literature research was conducted in Scopus, PubMed, and Web of Science. Experimental protocols (e.g., the aim of the study, number and type of participants, number and type of muscles, and tasks), methods (e.g., muscle synergy models and synergy extraction methods, signal processing methods), and the main findings of eligible studies were reported and discussed.

RESULTS

383 articles were screened and 51 were selected, which involved a total of 13 diseases and 748 patients and 1155 participants. Each study investigated on average 15 ± 10 patients. Four to forty-one muscles were included in the muscle synergy analysis. Point-to-point reaching was the most used task. The preprocessing of EMG signals and algorithms for synergy extraction varied among studies, and non-negative matrix factorization was the most used method. Five EMG normalization methods and five methods for identifying the optimal number of synergies were used in the selected papers. Most of the studies report that analyses on synergy number, structure, and activations provide novel insights on the physiopathology of motor control that cannot be gained with standard clinical assessments, and suggest that muscle synergies may be useful to personalize therapies and to develop new therapeutic strategies. However, in the selected studies synergies were used only for assessment; different testing procedures were used and, in general, study-specific modifications of muscle synergies were observed; single session or longitudinal studies mainly aimed at assessing stroke (71% of the studies), even though other pathologies were also investigated. Synergy modifications were either study-specific or were not observed, with few analyses available for temporal coefficients. Thus, several barriers prevent wider adoption of muscle synergy analysis including a lack of standardized experimental protocols, signal processing procedures, and synergy extraction methods. A compromise in the design of the studies must be found to combine the systematicity of motor control studies and the feasibility of clinical studies. There are however several potential developments that might promote the use of muscle synergy analysis in clinical practice, including refined assessments based on synergistic approaches not allowed by other methods and the availability of novel models. Finally, neural substrates of muscle synergies are discussed, and possible future research directions are proposed.

CONCLUSIONS

This review provides new perspectives about the challenges and open issues that need to be addressed in future work to achieve a better understanding of motor impairments and rehabilitative therapy using muscle synergies. These include the application of the methods on wider scales, standardization of procedures, inclusion of synergies in the clinical decisional process, assessment of temporal coefficients and temporal-based models, extensive work on the algorithms and understanding of the physio-pathological mechanisms of pathology, as well as the application and adaptation of synergy-based approaches to various rehabilitative scenarios for increasing the available evidence.

Muscle Synergies in People With Chronic Ankle Instability During Anticipated and Unanticipated Landing-Cutting Tasks

CONTEXT

Although neuromuscular deficits in people with chronic ankle instability (CAI) have been identified, previous researchers have mostly investigated the activation of multiple muscles in isolation. Investigating muscle synergies in people with CAI would provide information about the coordination and control of neuromuscular activation strategies and could supply important information for understanding and rehabilitating neuromuscular deficits in this population.

OBJECTIVE

To assess and compare muscle synergies using nonnegative matrix factorization in people with CAI and healthy control individuals as they performed different landing-cutting tasks.

DESIGN

Cross-sectional study.

SETTING

Laboratory.

PATIENTS OR OTHER PARTICIPANTS

A total of 11 people with CAI (5 men, 6 women; age = 22 ± 3 years, height = 1.68 ± 0.11 m, mass = 69.0 ± 19.1 kg) and 11 people without CAI serving as a healthy control group (5 men, 6 women; age = 23 ± 4 years, height = 1.74 ± 0.11 m, mass = 66.8 ± 15.5 kg) participated.

MAIN OUTCOME MEASURE(S)

Muscle synergies were extracted from electromyography of the lateral gastrocnemius, medial gastrocnemius, fibularis longus, soleus, and tibialis anterior (TA) muscles during anticipated and unanticipated landing-cutting tasks. The number of synergies, activation coefficients, and muscle-specific weighting coefficients were compared between groups and across tasks.

RESULTS

The number of muscle synergies was the same for each group and task. The CAI group exhibited greater TA weighting coefficients in synergy 1 than the control group (P = .02). In addition, both groups demonstrated greater fibularis longus (P = .03) weighting coefficients in synergy 2 during the unanticipated landing-cutting task than the anticipated landing-cutting task.

CONCLUSIONS

These results suggest that, although both groups used neuromuscular control strategies of similar complexity or dimensionality to perform the landing-cutting tasks, the CAI group displayed different muscle-specific weightings characterized by greater emphasis on TA function in synergy 1, which may reflect an effort to increase joint stability to compensate for ankle instability.

Similarity of hand muscle synergies elicited by transcranial magnetic stimulation and those found during voluntary movement

Converging evidence in human and animal models suggests that exogenous stimulation of the motor cortex (M1) elicits responses in the hand with similar modular structure to that found during voluntary grasping movements. The aim of this study was to establish the extent to which modularity in muscle responses to transcranial magnetic stimulation (TMS) to M1 resembles modularity in muscle activation during voluntary hand movements involving finger fractionation. Electromyography (EMG) was recorded from eight hand-forearm muscles in eight healthy individuals. Modularity was defined using non-negative matrix factorization to identify low-rank approximations (spatial muscle synergies) of the complex activation patterns of EMG data recorded during high-density TMS mapping of M1 and voluntary formation of gestures in the American Sign Language alphabet. Analysis of synergies revealed greater than chance similarity between those derived from TMS and those derived from voluntary movement. Both data sets included synergies dominated by single intrinsic hand muscles presumably to meet the demand for highly fractionated finger movement. These results suggest that corticospinal connectivity to individual intrinsic hand muscles may be combined with modular multimuscle activation via synergies in the formation of hand postures.NEW & NOTEWORTHY This is the first work to examine the similarity of modularity in hand muscle responses to transcranial magnetic stimulation (TMS) of the motor cortex and that derived from voluntary hand movement. We show that TMS-elicited muscle synergies of the hand, measured at rest, reflect those found in voluntary behavior involving finger fractionation. This work provides a basis for future work using TMS to investigate muscle activation modularity in the human motor system.

EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation

Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called "synergy extrapolation" or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were "unmeasured" for walking datasets collected from two individuals post-stroke-one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.

Evaluation of Methods for the Extraction of Spatial Muscle Synergies

Muscle synergies have been largely used in many application fields, including motor control studies, prosthesis control, movement classification, rehabilitation, and clinical studies. Due to the complexity of the motor control system, the full repertoire of the underlying synergies has been identified only for some classes of movements and scenarios. Several extraction methods have been used to extract muscle synergies. However, some of these methods may not effectively capture the nonlinear relationship between muscles and impose constraints on input signals or extracted synergies. Moreover, other approaches such as autoencoders (AEs), an unsupervised neural network, were recently introduced to study bioinspired control and movement classification. In this study, we evaluated the performance of five methods for the extraction of spatial muscle synergy, namely, principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), nonnegative matrix factorization (NMF), and AEs using simulated data and a publicly available database. To analyze the performance of the considered extraction methods with respect to several factors, we generated a comprehensive set of simulated data (ground truth), including spatial synergies and temporal coefficients. The signal-to-noise ratio (SNR) and the number of channels (NoC) varied when generating simulated data to evaluate their effects on ground truth reconstruction. This study also tested the efficacy of each synergy extraction method when coupled with standard classification methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and Random Forest (RF). The results showed that both SNR and NoC affected the outputs of the muscle synergy analysis. Although AEs showed better performance than FA in variance accounted for and PCA in synergy vector similarity and activation coefficient similarity, NMF and ICA outperformed the other three methods. Classification tasks showed that classification algorithms were sensitive to synergy extraction methods, while KNN and RF outperformed the other two methods for all extraction methods; in general, the classification accuracy of NMF and PCA was higher. Overall, the results suggest selecting suitable methods when performing muscle synergy-related analysis.

Evidence for shared neural information between muscle synergies and corticospinal efficacy

Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH), and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters, including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

How to Decide the Number of Gait Cycles in Different Low-Pass Filters to Extract Motor Modules by Non-negative Matrix Factorization During Walking in Chronic Post-stroke Patients

The motor modules during human walking are identified using non-negative matrix factorization (NNMF) from surface electromyography (EMG) signals. The extraction of motor modules in healthy participants is affected by the change in pre-processing of EMG signals, such as low-pass filters (LPFs); however, the effect of different pre-processing methods, such as the number of necessary gait cycles (GCs) in post-stroke patients with varying steps, remains unknown. We aimed to specify that the number of GCs influenced the motor modules extracted in the consideration of LPFs in post-stroke patients. In total, 10 chronic post-stroke patients walked at a self-selected speed on an overground walkway, while EMG signals were recorded from the eight muscles of paretic lower limb. To verify the number of GCs, five GC conditions were set, namely, 25 (reference condition), 20, 15, 10, and 5 gate cycles with three LPFs (4, 10, and 15 Hz). First, the number of modules, variability accounted for (VAF), and muscle weightings extracted by the NNMF algorithm were compared between the conditions. Next, a modified NNMF algorithm, in which the activation timing profiles among different GCs were unified, was performed to compare the muscle weightings more robustly between GCs. The number of motor modules was not significantly different, regardless of the GCs. The difference in VAF and muscle weightings in the different GCs decreased with the LPF of 4 Hz. Muscle weightings in 15 GCs or less were significantly different from those in 25 GCs using the modified NNMF. Therefore, we concluded that the variability extracted motor modules by different GCs was suppressed with lower LPFs; however, 20 GCs were needed for more representative extraction of motor modules during walking in post-stroke patients.

Whole-Body Adaptive Functional Electrical Stimulation Kinesitherapy Can Promote the Restoring of Physiological Muscle Synergies for Neurological Patients

Background: Neurological diseases and traumas are major factors that may reduce motor functionality. Functional electrical stimulation is a technique that helps regain motor function, assisting patients in daily life activities and in rehabilitation practices. In this study, we evaluated the efficacy of a treatment based on whole-body Adaptive Functional Electrical Stimulation Kinesitherapy (AFESK™) with the use of muscle synergies, a well-established method for evaluation of motor coordination. The evaluation is performed on retrospectively gathered data of neurological patients executing whole-body movements before and after AFESK-based treatments. Methods: Twenty-four chronic neurologic patients and 9 healthy subjects were recruited in this study. The patient group was further subdivided in 3 subgroups: hemiplegic, tetraplegic and paraplegic. All patients underwent two acquisition sessions: before treatment and after a FES based rehabilitation treatment at the VIKTOR Physio Lab. Patients followed whole-body exercise protocols tailored to their needs. The control group of healthy subjects performed all movements in a single session and provided reference data for evaluating patients’ performance. sEMG was recorded on relevant muscles and muscle synergies were extracted for each patient’s EMG data and then compared to the ones extracted from the healthy volunteers. To evaluate the effect of the treatment, the motricity index was measured and patients’ extracted synergies were compared to the control group before and after treatment. Results: After the treatment, patients’ motricity index increased for many of the screened body segments. Muscle synergies were more similar to those of healthy people. Globally, the normalized synergy similarity in respect to the control group was 0.50 before the treatment and 0.60 after (p < 0.001), with improvements for each subgroup of patients. Conclusions: AFESK treatment induced favorable changes in muscle activation patterns in chronic neurologic patients, partially restoring muscular patterns similar to healthy people. The evaluation of the synergic relationships of muscle activity when performing test exercises allows to assess the results of rehabilitation measures in patients with impaired locomotor functions.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements

OBJECTIVE

Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements.

APPROACH

Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted.

MAIN RESULTS

The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes.

SIGNIFICANCE

The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Effect of Robotic Exoskeleton Motion Constraints on Upper Limb Muscle Synergies: A Case Study

Evidence exists that changes in composition, timing, and number of muscle synergies can be correlated to functional changes resulting from neurological injury. These changes can also serve as an indicator of level of motor impairment. As such, synergy analysis can be used as an assessment tool for robotic rehabilitation. However, it is unclear whether using a rehabilitation robot to isolate limb movements during training affects the subject's muscle synergies, which would affect synergy-based assessments. In this case study, electromyographic (EMG) data were collected to analyze muscle synergies generated during single degree-of-freedom (DoF) elbow and wrist movements performed by a single healthy subject in a four DoF robotic exoskeleton. For each trial, the subject was instructed to move a single DoF from a neutral position out to a target and back while the remaining DoFs were held in a neutral position by either the robot (constrained) or the subject (unconstrained). Four factorization methods were used to calculate muscle synergies for both types of trials: concatenation, averaging, single trials, and bootstrapping. The number of synergies was chosen to achieve 90% global variability accounted for. Our preliminary results indicate that muscle synergy composition and timing were highly similar for constrained and unconstrained trials, though some differences between the four factorization methods existed. These differences could be explained by higher trial-to-trial EMG variability for the unconstrained trials. These results suggest that using a robotic exoskeleton to constrain limb movements during robotic training may not alter a subject's muscle synergies, at least for healthy subjects.

Recording activity in proximal muscle networks with surface EMG in assessing infant motor development

OBJECTIVE

To develop methods for recording and analysing infant's proximal muscle activations.

METHODS

Surface electromyography (sEMG) of truncal muscles was recorded in three months old infants (N = 18) during spontaneous movement and controlled postural changes. The infants were also divided into two groups according to motor performance. We developed an efficient method for removing dynamic cardiac artefacts to allow i) accurate estimation of individual muscle activations, as well as ii) quantitative characterization of muscle networks.

RESULTS

The automated removal of cardiac artefacts allowed quantitation of truncal muscle activity, which showed predictable effects during postural changes, and there were differences between high and low performing infants.The muscle networks showed consistent change in network density during spontaneous movements between supine and prone position. Moreover, activity correlations in individual pairs of back muscles linked to infant́s motor performance.

CONCLUSIONS

The hereby developed sEMG analysis methodology is feasible and may disclose differences between high and low performing infants. Analysis of the muscle networks may provide novel insight to central control of motility.

SIGNIFICANCE

Quantitative analysis of infant's muscle activity and muscle networks holds promise for an objective neurodevelopmental assessment of motor system.

Common motor patterns of asymmetrical and symmetrical bipedal gaits

Background

Synergy modules have been used to describe activation of lower limb muscles during locomotion and hence to understand how the system controls movement. Walking and running have been shown shared synergy patterns suggesting common motor control of both symmetrical gaits. Unilateral skipping, an equivalent gait to the quadrupedal gallop in humans, has been defined as the third locomotion paradigm but the use by humans is limited due to its high metabolic cost. Synergies in skipping have been little investigated. In particular, to the best of our knowledge, the joint study of both trailing and leading limbs has never been addressed before.

Research question

How are organized muscle activation patterns in unilateral skipping? Are they organized in the same way that in symmetrical gaits? If yes, which are the muscle activation patterns in skipping that make it a different gait to walking or running? In the present research, we investigate if there are shared control strategies for all gaits in locomotion. Addressing these questions in terms of muscle synergies could suggest possible determinants of the scarce use of unilateral skipping in humans.

Methods

Electromyographic data of fourteen bilateral muscles were collected from volunteers while performing walking, running and unilateral skipping on a treadmill. Also, spatiotemporal gait parameters were computed from 3D kinematics. The modular composition and activation timing extracted by non-negative matrix factorization were analyzed to detect similarities and differences among symmetrical gaits and unilateral skipping.

Results

Synergy modules showed high similarity throughout the different gaits and between trailing and leading limbs during unilateral skipping. The synergy associated with the propulsion force operated by calf muscles was anticipated in bouncing gaits. Temporal features of synergies in the leading leg were very similar to those observed for running. The different role of trailing and leading legs in unilateral skipping was reflected by the different timing in two modules. Activation for weight acceptance was anticipated and extended in the trailing leg, preparing the body for landing impact after the flight phase. A different behaviour was detected in the leading leg, which only deals with a pendular weight transference.

Significance

The evidence gathered in this work supports the hypothesis of shared modules among symmetrical and asymmetrical gaits, suggesting a common motor control despite of the infrequent use of unilateral skipping in humans. Unilateral skipping results from phase-shifted activation of similar muscular groups used in symmetrical gaits, without the need for new muscular groups. The high and anticipated muscle activation in the trailing leg for landing could be the key distinctive event of unilateral skipping.

Analysis Choices Impact Movement Evaluation: A Multi-Aspect Inferential Method Applied to Kinematic Curves of Vertical Hops in Knee-Injured and Asymptomatic Persons

Three-dimensional human motion analysis provides in-depth understanding in order to optimize sports performance or rehabilitation following disease or injury. Recent developments of statistical methods for functional data allow for novel ways to analyze often complex biomechanical data. Even so, for such methods as well as for traditional well-established statistical methods, the interpretations of the results may be influenced by analysis choices made prior to the analysis. We evaluated the consequences of three such choices when comparing one-leg vertical hop (OLVH) performance in individuals who had ruptured their anterior cruciate ligament (ACL), to that of asymptomatic controls, and also athletes. Kinematic data were analyzed using a statistical approach for functional data, targeting entire curve data. This was done not only for one joint at a time but also for multiple lower limb joints and movement planes simultaneously using a multi-aspect methodology, testing for group differences while also accounting for covariates. We present the results of when an individual representative curve out of three available was either: (1) a mean curve (Mean), (2) a curve from the highest hop (Max), or (3) a curve describing the variability (Var), as a representation of performance stability. We also evaluated choice of sample leg comparison; e.g., ACL-injured leg compared to either the dominant or non-dominant leg of asymptomatic groups. Finally, we explored potential outcome effects of different combinations of included joints. There were slightly more pronounced group differences when using Mean compared to Max, while the specifics of the observed differences depended on the outcome variable. For Var there were less significant group differences. Generally, there were more disparities throughout the hop movement when comparing the injured leg to the dominant leg of controls, resulting in e.g., group differences for trunk and ankle kinematics, for both Mean and Max. When the injured leg was instead compared to the non-dominant leg of controls, there were trunk, hip and knee joint differences. For a more stringent comparison, we suggest considering to compare the injured leg to the non-dominant leg. Finally, the multiple-joint analyses were coherent with the single-joint analyses. The direct effects of analysis choices can be explored interactively by the reader in the Supplementary Material. To summarize, the choices definitively have an impact on the interpretation of a hop test results commonly used in rehabilitation following knee injuries. We therefore strongly recommend well-documented methodological analysis choices with regards to comparisons and representative values of the measures of interests.

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

Quantification of muscle coordination underlying basic shoulder movements using muscle synergy extraction

Numerous muscles around the shoulder joint are required to work in a coordinated manner, even when a basic shoulder movement is executed. Muscle synergy can be utilized as an index to determine muscle coordination. The purpose of the present study was to investigate the muscle coordination among different shoulder muscles underlying basic shoulder movements based on muscle synergy. Thirteen men performed 14 multiplanar shoulder movements; five movements were associated with elevation and lowering, while five were associated with horizontal abduction and adduction. The four additional movements were simple rotations at different positions. Muscle activity was measured from 12 muscle portions using surface electromyography. Using the dimensionality reduction technique, synergies were extracted first for each movement separately ("separate" synergies), and then for the global dataset (containing all movements; "global" synergies). The least number that provided 90% of the variance accounted for was selected as the optimal number of synergies. For each subject, approximately two separate synergies and approximately six global synergies with small residual values were extracted from the separate and global electromyography datasets, respectively. Specific patterns of these muscle synergies in each task were observed during each movement. In the cross-validation method, six global synergies explained 88.0 ± 1.3% of the global dataset. These findings indicate that muscle activities underlying basic shoulder movements are expressed as six units, and these units could be proxies for shoulder muscle coordination.

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.

Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies

Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data

Background. Muscle synergy analysis is an approach to understand the neurophysiological mechanisms behind the hypothesized ability of the Central Nervous System (CNS) to reduce the dimensionality of muscle control. The muscle synergy approach is also used to evaluate motor recovery and the evolution of the patients’ motor performance both in single-session and longitudinal studies. Synergy-based assessments are subject to various sources of variability: natural trial-by-trial variability of performed movements, intrinsic characteristics of subjects that change over time (e.g., recovery, adaptation, exercise, etc.), as well as experimental factors such as different electrode positioning. These sources of variability need to be quantified in order to resolve challenges for the application of muscle synergies in clinical environments. The objective of this study is to analyze the stability and similarity of extracted muscle synergies under the effect of factors that may induce variability, including inter- and intra-session variability within subjects and inter-subject variability differentiation. The analysis was performed using the comprehensive, publicly available hand grasp NinaPro Database, featuring surface electromyography (EMG) measures from two EMG electrode bracelets. Methods. Intra-session, inter-session, and inter-subject synergy stability was analyzed using the following measures: variance accounted for (VAF) and number of synergies (NoS) as measures of reconstruction stability quality and cosine similarity for comparison of spatial composition of extracted synergies. Moreover, an approach based on virtual electrode repositioning was applied to shed light on the influence of electrode position on inter-session synergy similarity. Results. Inter-session synergy similarity was significantly lower with respect to intra-session similarity, both considering coefficient of variation of VAF (approximately 0.2–15% for inter vs. approximately 0.1% to 2.5% for intra, depending on NoS) and coefficient of variation of NoS (approximately 6.5–14.5% for inter vs. approximately 3–3.5% for intra, depending on VAF) as well as synergy similarity (approximately 74–77% for inter vs. approximately 88–94% for intra, depending on the selected VAF). Virtual electrode repositioning revealed that a slightly different electrode position can lower similarity of synergies from the same session and can increase similarity between sessions. Finally, the similarity of inter-subject synergies has no significant difference from the similarity of inter-session synergies (both on average approximately 84–90% depending on selected VAF). Conclusion. Synergy similarity was lower in inter-session conditions with respect to intra-session. This finding should be considered when interpreting results from multi-session assessments. Lastly, electrode positioning might play an important role in the lower similarity of synergies over different sessions.

Differences in Muscle Synergy Symmetry Between Subacute Post-stroke Patients With Bioelectrically-Controlled Exoskeleton Gait Training and Conventional Gait Training

Understanding the reorganization of the central nervous system after stroke is an important endeavor in order to design new therapies in gait training for stroke patients. Current clinical evaluation scores and gait velocity are insufficient to describe the state of the nervous system, and one aspect where this is lacking is in the quantification of gait symmetry. Previous studies have pointed out that spatiotemporal gait asymmetries are commonly observed in stroke patients with hemiparesis. Such asymmetries are known to cause long-term complications like joint pain and deformation. Recent studies also indicate that spatiotemporal measures showed that gait symmetry worsens after discharge from therapy. This study shows that muscle synergy analysis can be used to quantify gait symmetry and compliment clinical measures. Surface EMG was collected from lower limb muscles of subacute post-stroke patients (with an onset of around 14 days) from two groups, one undergoing robotic-assisted therapy (known as HAL group) and the other undergoing conventional therapy (known as Control group). Muscle synergies from the paretic and non-paretic limb were extracted with Non-Negative Matrix Factorization (NNMF) and compared with each other to obtain a gait symmetry index over therapy sessions. Gait events were tracked with motion tracking (for the HAL group) or foot pressure sensors (for the conventional therapy group). Patients from both groups were assessed over a 3-weeks long gait training program. Results indicated that there were no differences in muscle synergy symmetry for both groups of patients. However, the timing of muscle synergies were observed to be symmetrical in the HAL group, but not for the Control group. Intergroup comparisons of symmetry in muscle synergies and their timings were not significantly different. This could be due to a large variability in recovery in the Control group. Finally, stance time ratio was not observed to improve in both groups after their respective therapies. Interestingly, FIM and FMA scores of both groups were observed to improve after their respective therapies. Analysis of muscle coordination could reveal mechanisms of gait symmetry which could otherwise be difficult to observe with clinical scores.

Walking characteristics including mild motor paralysis and slow walking speed in post-stroke patients

Walking speed is strongly influenced by the severity of motor paralysis in post-stroke patients. Nevertheless, some patients with mild motor paralysis still walk slowly. Factors associated with this difference in walking speed have not been elucidated. To confirm walking characteristics of patients with mild motor paralysis and slow walking speed, this study identified patient subgroups based on the association between the severity of motor paralysis and walking speed. Fugl-Meyer assessment synergy score (FMS) and the walking speed were measured (n = 42), and cluster analysis was performed based on the association between FMS and walking speed to identify the subgroups. FMS and walking speed were associated (ρ = 0.50); however, some patients walked slowly despite only mild motor paralysis. Cluster analysis using FMS and walking speed as the main variables classified patients into subgroups. Patients with mild motor paralysis (FMS: 18.4 ± 2.09 points) and slow walking speed (0.28 ± 0.14 m/s) exhibited poorer trunk stability, increased co-contraction of the shank muscle, and increased intramuscular coherence in walking compared to other clusters. This group was identified by their inability to fully utilize the residual potential of motor function. In walking training, intervention in instability and excessive cortical control may be effective.

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.

Age-Related Differences in Muscle Synergy Organization during Step Ascent at Different Heights and Directions

The aim of this study was to explore the underlying age-related differences in dynamic motor control during different step ascent conditions using muscle synergy analysis. Eleven older women (67.0 y ± 2.5) and ten young women (22.5 y ± 1.6) performed stepping in forward and lateral directions at step heights of 10, 20 and 30 cm. Surface electromyography was obtained from 10 lower limb and torso muscles. Non-negative matrix factorization was used to identify sets of (n) synergies across age groups and stepping conditions. In addition, variance accounted for (VAF) by the detected number of synergies was compared to assess complexity of motor control. Finally, correlation coefficients of muscle weightings and between-subject variability of the temporal activation patterns were calculated and compared between age groups and stepping conditions. Four synergies accounted for >85% VAF across age groups and stepping conditions. Age and step height showed a significant negative correlation with VAF during forward stepping but not lateral stepping, with lower VAF indicating higher synergy complexity. Muscle weightings showed higher similarity across step heights in older compared to young women. Neuromuscular control of young and community-dwelling older women could not be differentiated based on the number of synergies extracted. Additional analyses of synergy structure and complexity revealed subtle age- and step-height-related differences, indicating that older women rely on more complex neuromuscular control strategies.

A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects

BACKGROUND

Recently, muscle synergy analysis has become a standard methodology for extracting coordination patterns from electromyographic (EMG) signals, and for the evaluation of motor control strategies in many contexts. Most previous studies have characterized upper-limb muscle synergies across a limited set of reaching movements. With the aim of future uses in motor control, rehabilitation and other fields, this study provides a comprehensive characterization of muscle synergies in a large set of upper-limb tasks and also considers inter-individual and environmental variability.

METHODS

Sixteen healthy subjects performed upper-limb hand exploration movements for a comprehensive mapping of the upper-limb workspace, which was divided into several sectors (Frontal, Right, Left, Horizontal, and Up). EMGs from representative upper-limb muscles and kinematics were recorded to extract muscle synergies and explore the composition, repeatability and similarity of spatial synergies across subjects and movement directions, in a context of high variability of motion.

RESULTS

Even in a context of high variability, a reduced set of muscle synergies may reconstruct the original EMG envelopes. Composition, repeatability and similarity of synergies were found to be shared across subjects and sectors, even if at a lower extent than previously reported.

CONCLUSION

Extending the results of previous studies, which were performed on a smaller set of conditions, a limited number of muscle synergies underlie the execution of a large variety of upper-limb tasks. However, the considered spatial domain and the variability seem to influence the number and composition of muscle synergies. Such detailed characterization of the modular organization of the muscle patterns for upper-limb control in a large variety of tasks may provide a useful reference for studies on motor control, rehabilitation, industrial applications, and sports.

Modular Control of Human Movement During Running: An Open Access Data Set

The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the 20th century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of 5 to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males and 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization (NMF) and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using NMF. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing, and late swing. We foresee many possible applications of our data that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modeling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods. All the data is available at Zenodo (doi: 10.5281/zenodo.1254380).

Using a Module-Based Analysis Framework for Investigating Muscle Coordination during Walking in Individuals Poststroke: A Literature Review and Synthesis

Factorization methods quantitatively group electromyographic signals from several muscles during dynamic tasks into multiple modules where each module consists of muscles that are coactive during the movement. Module-based analyses may provide an analytical framework for testing theories of poststroke motor control recovery based on one's ability to move independently from mass flexion-extension muscle group coactivation. Such a framework may be useful for understanding the causality between underlying neural impairments, biomechanical function, and walking performance in individuals poststroke. Our aim is to synthesize current evidence regarding the relationships between modules, gait mechanics, and rehabilitation in individuals poststroke. We synthesized eleven studies that performed module-based analyses during walking tasks for individuals poststroke. Modules were primarily identified by nonnegative matrix factorization, and fewer modules correlated with poor walking performance on biomechanical and clinical measures. Fewer modules indicated reduced ability to control individual muscle timing during paretic leg stance. There was evidence that rehabilitation can lead to the use of more and/or better-timed modules. While future work will need to establish the ability of modules to identify impairment mechanisms, they appear to offer a promising analytical approach for evaluating motor control.

Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention

Gait disturbance is commonly associated with stroke, which is a serious neurological disease. With current technology, various exoskeletons have been developed to provide therapy, leading to many studies evaluating the use of such exoskeletons as an intervention tool. Although these studies report improvements in patients who had undergone robotic intervention, they are usually reported with clinical assessment, which are unable to characterize how muscle activations change in patients after robotic intervention. We believe that muscle activations can provide an objective view on gait performance of patients. To quantify improvement of lateral symmetry before and after robotic intervention, muscle synergy analysis with Non-Negative Matrix Factorization was used to evaluate patients' EMG data. Eight stroke patients in their acute phase were evaluated before and after a course of robotic intervention with the Hybrid Assistive Limb (HAL), lasting over 3 weeks. We found a significant increase in similarity between lateral synergies of patients after robotic intervention. This is associated with significant improvements in gait measures like walking speed, step cadence, stance duration percentage of gait cycle. Clinical assessments [Functional Independence Measure-Locomotion (FIM-Locomotion), FIM-Motor (General), and Fugl-Meyer Assessment-Lower Extremity (FMA-LE)] showed significant improvements as well. Our study shows that muscle synergy analysis can be a good tool to quantify the change in neuromuscular coordination of lateral symmetry during walking in stroke patients.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

Electromyography Exposes Heterogeneity in Muscle Co-Contraction following Stroke

Walking after stroke is often described as requiring excessive muscle co-contraction, yet, evidence that co-contraction is a ubiquitous motor control strategy for this population remains inconclusive. Co-contraction, the simultaneous activation of agonist and antagonist muscles, can be assessed with electromyography (EMG) but is often described qualitatively. Here, our goal is to determine if co-contraction is associated with gait impairments following stroke. Fifteen individuals with chronic stroke and nine healthy controls walked on an instrumented treadmill at self-selected speed. Surface EMGs were collected from the medial gastrocnemius (MG), soleus (SOL), and tibialis anterior (TA) of each leg. EMG envelope amplitudes were assessed in three ways: (1) no normalization, (2) normalization to the maximum value across the gait cycle, or (3) normalization to maximal M-wave. Three co-contraction indices were calculated across each agonist/antagonist muscle pair (MG/TA and SOL/TA) to assess the effect of using various metrics to quantify co-contraction. Two factor ANOVAs were used to compare effects of group and normalization for each metric. Co-contraction during the terminal stance (TSt) phase of gait is not different between healthy controls and the paretic leg of individuals post-stroke, regardless of the metric used to quantify co-contraction. Interestingly, co-contraction was similar between M-max and non-normalized EMG; however, normalization does not impact the ability to resolve group differences. While a modest correlation is revealed between the amount of TSt co-contraction and walking speed, the relationship is not sufficiently strong to motivate further exploration of a causal link between co-contraction and walking function after stroke. Co-contraction does not appear to be a common strategy employed by individuals after stroke. We recommend exploration of alternative EMG analysis approaches in an effort to learn more about the causal mechanisms of gait impairment following stroke.