Dimensionality of joint torques and muscle patterns for reaching

Deactivation and collective phasic muscular tuning for pointing direction: Insights from machine learning

Arm movements in our daily lives have to be adjusted for several factors in response to the demands of the environment, for example, speed, direction or distance. Previous research has shown that arm movement kinematics is optimally tuned to take advantage of gravity effects and minimize muscle effort in various pointing directions and gravity contexts. Here we build upon these results and focus on muscular adjustments. We used Machine Learning to analyze the ensemble activities of multiple muscles recorded during pointing in various directions. The advantage of such a technique would be the observation of patterns in collective muscular activity that may not be noticed using univariate statistics. By providing an index of multimuscle activity, the Machine Learning (ML) analysis brought to light several features of tuning for pointing direction. In attempting to trace tuning curves, all comparisons were done with respects to pointing in the horizontal, gravity free plane. We demonstrated that tuning for direction does not take place in a uniform fashion but in a modular manner in which some muscle groups play a primary role. The antigravity muscles were more finely tuned to pointing direction than the gravity muscles. Of note, was their tuning during the first half of downward pointing. As the antigravity muscles were deactivated during this phase, it supported the idea that deactivation is not an on-off function but is tuned to pointing direction. Further support for the tuning of the negative portions of the phasic EMG was provided by the observation of progressively improving classification accuracies with increasing angular distance from the horizontal. We also demonstrated that the durations of these negative phases, without information on their amplitudes, is tuned to pointing directions. Overall, these results show that the motor system tunes muscle commands to exploit gravity effects and reduce muscular effort. It quantitatively demonstrates that phasic EMG negativity is an essential feature of muscle control. The ML analysis was done using Linear Discriminant analysis (LDA) and Support Vector Machines (SVM). The two led to the same conclusions concerning the movements being investigated, hence showing that the former, computationally inexpensive technique is a viable tool for regular investigation of motor control.

A tensor decomposition reveals ageing-induced differences in muscle and grip-load force couplings during object lifting

Do motor patterns of object lifting movements change as a result of ageing? Here we propose a methodology for the characterization of these motor patterns across individuals of different age groups. Specifically, we employ a bimanual grasp-lift-replace protocol with younger and older adults and combine measurements of muscle activity with grip and load forces to provide a window into the motor strategies supporting effective object lifts. We introduce a tensor decomposition to identify patterns of muscle activity and grip-load force ratios while also characterizing their temporal profiles and relative activation across object weights and participants of different age groups. We then probe age-induced changes in these components. A classification analysis reveals three motor components that are differentially recruited between the two age groups. Linear regression analyses further show that advanced age and poorer manual dexterity can be predicted by the coupled activation of forearm and hand muscles which is associated with high levels of grip force. Our findings suggest that ageing may induce stronger muscle couplings in distal aspects of the upper limbs, and a less economic grasping strategy to overcome age-related decline in manual dexterity.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

BACKGROUND AND OBJECTIVE

A new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach.

METHODS

MMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies.

RESULTS

As an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License.

CONCLUSIONS

Thanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Characteristics of multi-channel intermuscular directional coupling based on time-varying partial directional coherence analysis

The human body transmits directional information between muscles during upper limb movements, and this will be particularly evident when the dominant muscle changes during movement transitions. By capturing the electromyography (EMG) signals of wrist flexion and extension continuous transition movements, we investigated the characteristics of multichannel intermuscular directional coupling and directional information transmission, and consequently explored the control mechanism of Central nervous system (CNS) and the coordination mechanism of motor muscles. Multi-channel EMG was collected from 12 healthy subjects under continuous translational movements of wrist flexion and extension, and the time-varying biased directional coherence analysis (TVPDC) model was constructed using partial directional coherence analysis (PDC) frequency domain directionality to study the directional information transfer characteristics in the time-frequency domain, screen closely related muscle pairs and perform directional coupling significance analysis. Palmaris longus (PL) played a dominant role under wrist flexion movements(WF), Extensor Carpi Radialis (ECR) played a dominant role under wrist extension movements(WE), and the remaining muscles responded to them with information and Biceps Brachii (BB) played a responsive role throughout the movement; flexor pairs had the highest positive coupling values in the beta band during Conversion action1 (MC1) and WF phases, and extensor pairs had the highest positive coupling values in the gamma band during Conversion action2(MC2) phase and the highest coupling values in the beta band during WE phase. TVPDC can effectively analyze the multichannel intermuscular directional coupling and information transmission relationship of surface electromyography under wrist flexion and extension transition movements, providing a reference for exploring the control mechanism of CNS and abnormal control mechanism in patients with motor dysfunction in a new perspective.

Generalizability of muscle synergies in isometric force generation versus point-to-point reaching in the human upper extremity workspace

Isometric force generation and kinematic reaching in the upper extremity has been found to be represented by a limited number of muscle synergies, even across task-specific variations. However, the extent of the generalizability of muscle synergies between these two motor tasks within the arm workspace remains unknown. In this study, we recorded electromyographic (EMG) signals from 13 different arm, shoulder, and back muscles of ten healthy individuals while they performed isometric and kinematic center-out target matches to one of 12 equidistant directional targets in the horizontal plane and at each of four starting arm positions. Non-negative matrix factorization was applied to the EMG data to identify the muscle synergies. Five and six muscle synergies were found to represent the isometric force generation and point-to-point reaches. We also found that the number and composition of muscle synergies were conserved across the arm workspace per motor task. Similar tuning directions of muscle synergy activation profiles were observed at different starting arm locations. Between the isometric and kinematic motor tasks, we found that two to four out of five muscle synergies were common in the composition and activation profiles across the starting arm locations. The greater number of muscle synergies that were involved in achieving a target match in the reaching task compared to the isometric task may explain the complexity of neuromotor control in arm reaching movements. Overall, our results may provide further insight into the neuromotor compartmentalization of shared muscle synergies between two different arm motor tasks and can be utilized to assess motor disabilities in individuals with upper limb motor impairments.

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 activation patterns and muscle synergies reflect different modes of coordination during upper extremity movement

A core issue in motor control is how the central nervous system generates and selects the muscle activation patterns necessary to achieve a variety of behaviors and movements. Extensive studies have verified that it is the foundation to induce a complex movement by the modular combinations of several muscles with a synergetic relationship. However, a few studies focus on the synergetic similarity and dissimilarity among different types of movements, especially for the upper extremity movements. In this study, we introduced the non-negative matrix factorization (NMF) method to explore the muscle activation patterns and synergy structure under 6 types of movements, involving the hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), supination (SU), and pronation (PR). For this, we enrolled 10 healthy subjects to record the electromyography signal for NMF calculation. The results showed a highly modular similarity of the muscle synergy among subjects under the same movement. Furthermore, Spearman's correlation analysis indicated significant similarities among HO-WE, HO-SU, and WE-SU (p < 0.001). Additionally, we also found shared synergy and special synergy in activation patterns among different movements. This study confirmed the theory of modular structure in the central nervous system, which yields a stable synergetic pattern under the same movement. Our findings on muscle synergy will be of great significance to motor control and even to clinical assessment techniques.

The Number and Structure of Muscle Synergies Depend on the Number of Recorded Muscles: A Pilot Simulation Study with OpenSim

The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an "average" condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same R² level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.

Number of trials and data structure affect the number and components of muscle synergies in upper-limb reaching movements

Objective.Due to the variability of human movements, muscle activations vary among trials and subjects. However, few studies investigated how data organization methods for addressing variability impact the extracted muscle synergies.Approach.Fifteen healthy subjects performed a large set of upper limb multi-directional point-to-point reaching movements. Then, the study extracted muscle synergies under different data settings and investigated how data structure prior to synergy extraction, namely concatenation, averaging, and single trial, the number of considered trials, and the number of reaching directions affected the number and components of muscle synergies.Main results.The results showed that the number and components of synergies were significantly affected by the data structure. The concatenation method identified the highest number of synergies, and the averaging method usually found a smaller number of synergies. When the concatenated trials or reaching directions was lower than a minimum value, the number of synergies increased with the increase of the number of trials or reaching directions; however, when the number of trials or reaching directions reached a threshold, the number of synergies was usually constant or with less variation even when novel directions and trials were added. Similarity analysis also showed a slight increase when the number of trials or reaching directions was lower than a threshold. This study recommends that at least five trials and four reaching directions and the concatenation method are considered in muscle synergies analysis during upper limb tasks.Significance.This study makes the researchers focus on the variability analysis induced by the diseases rather than the techniques applied for synergies analysis and promotes applications of muscle synergies in clinical scenarios.

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements

Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.

Muscle torques and joint accelerations provide more sensitive measures of poststroke movement deficits than joint angles

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The impact of stroke on the complex multijoint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested whether poststroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of poststroke motor deficits than joint angles. The motion of 22 participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamic analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multijoint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that muscle torques characterize individual reaching movements with higher information content than joint angles do. Moreover, muscle torques enable distinguishing the individual motor deficits caused by aging or stroke from the typical differences in reaching between healthy individuals. Similar results were obtained using metrics derived from joint accelerations. This novel quantitative assessment method may be used in conjunction with home-based gaming motion capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions.NEW & NOTEWORTHY Functional deficits seen in task performance have biomechanical underpinnings, seen only through the analysis of forces. Our study has shown that estimating muscle moments can quantify with high-sensitivity poststroke deficits in intersegmental coordination. An assessment developed based on this method could help quantify less observable deficits in mildly affected stroke patients. It may also bridge the gap between evidence from studies of constrained or robotically manipulated movements and research with functional and unconstrained movements.

Investigation of Power Specific Motor Primitives in an Upper Limb Rotational Motion

The purpose of this study was to investigate muscle synergies (MS) during upper limb cycling motion across power levels (20, 40, 60, 80, 100, and 120 watts). The MS hypothesis is important to the understanding of modular control for human movements. In this study, we explore its importance in execution of phasic movements at various power levels. Electromyographic (EMG) signals were recorded from 7 upper limb muscles during cycling for 30s on a hand-cycle ergometer. A Non-Negative Matrix factorization (NNMF) algorithm was used to extract MS. Cosine similarity was used to compare the MS and cross-correlation was used to compare activation coefficients. We found that the number and structure of synergies were consistent across power levels while admitting modulation in their activation coefficients. A total of three shared MS explaining ≥95% of the variance accounted for (VAF) represented push and pull mechanism during cyclic motion.

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.

Plasticity of muscle synergies through fractionation and merging during development and training of human runners

Complex motor commands for human locomotion are generated through the combination of motor modules representable as muscle synergies. Recent data have argued that muscle synergies are inborn or determined early in life, but development of the neuro-musculoskeletal system and acquisition of new skills may demand fine-tuning or reshaping of the early synergies. We seek to understand how locomotor synergies change during development and training by studying the synergies for running in preschoolers and diverse adults from sedentary subjects to elite marathoners, totaling 63 subjects assessed over 100 sessions. During development, synergies are fractionated into units with fewer muscles. As adults train to run, specific synergies coalesce to become merged synergies. Presences of specific synergy-merging patterns correlate with enhanced or reduced running efficiency. Fractionation and merging of muscle synergies may be a mechanism for modifying early motor modules (Nature) to accommodate the changing limb biomechanics and influences from sensorimotor training (Nurture).

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.

Muscle Synergies During Walking in Children With Cerebral Palsy: A Systematic Review

Background: Walking problems in children with cerebral palsy (CP) can in part be explained by limited selective motor control. Muscle synergy analysis is increasingly used to quantify altered neuromuscular control during walking. The early brain injury in children with CP may lead to a different development of muscle synergies compared to typically developing (TD) children, which might characterize the abnormal walking patterns.

Objective: The overarching aim of this review is to give an overview of the existing studies investigating muscle synergies during walking in children with CP compared to TD children. The main focus is on how muscle synergies differ between children with CP and TD children, and we examine the potential of muscle synergies as a measure to quantify and predict treatment outcomes.

Methods: Bibliographic databases were searched by two independent reviewers up to 22 April 2019. Studies were included if the focus was on muscle synergies of the lower limbs during walking, obtained by a matrix factorization algorithm, in children with CP.

Results: The majority (n = 12) of the 16 included studies found that children with CP recruited fewer muscle synergies during walking compared to TD children, and several studies (n = 8) showed that either the spatial or temporal structure of the muscle synergies differed between children with CP and TD children. Variability within and between subjects was larger in children with CP than in TD children, especially in more involved children. Muscle synergy characteristics before treatments to improve walking function could predict treatment outcomes (n = 3). Only minimal changes in synergies were found after treatment.

Conclusions: The findings in this systematic review support the idea that children with CP use a simpler motor control strategy compared to TD children. The use of muscle synergy analysis as a clinical tool to quantify altered neuromuscular control and predict clinical outcomes seems promising. Further investigation on this topic is necessary, and the use of muscle synergies as a target for development of novel therapies in children with CP could be explored.

Visual Feedback of Object Motion Direction Influences the Timing of Grip Force Modulation During Object Manipulation

During manipulation, object slipping is prevented by modulating the grip force (GF) in synchrony with motion-related inertial forces, i.e., load force (LF). However, due to conduction delays of the sensory system, GF must be modulated in advance based on predictions of LF changes. It has been proposed that such predictive force control relies on internal representations, i.e., internal models, of the relation between the dynamic of the environment and movement kinematics. Somatosensory and visual feedback plays a primary role in building these internal representations. For instance, it has been shown that manipulation-dependent somatosensory signals contribute to building internal representations of gravity in normal and altered gravitational contexts. Furthermore, delaying the timing of visual feedback of object displacement has been shown to affect GF. Here, we explored whether and the extent to which spatial features of visual feedback movement, such as motion direction, may contribute to GF control. If this were the case, a spatial mismatch between actual (somatosensory) and visual feedback of object motion would elicit changes in GF modulation. We tested this hypothesis by asking participants to generate vertical object movements while visual feedback of object position was congruent (0° rotation) or incongruent (180° or 90°) with the actual object displacement. The role of vision on GF control was quantified by the temporal shift of GF modulation as a function of visual feedback orientation and actual object motion direction. GF control was affected by visual feedback when this was incongruent in the vertical (180°), but not horizontal dimension. Importantly, 180° visual feedback rotation delayed and anticipated GF modulation during upward and downward actual movements, respectively. Our findings suggest that during manipulation, spatial features of visual feedback motion are used to predict upcoming LF changes. Furthermore, the present study provides evidence that an internal model of gravity contributes to GF control by influencing sensory reweighting processes during object manipulation.

Does the cerebellum shape the spatiotemporal organization of muscle patterns? Insights from subjects with cerebellar ataxias

The role of the cerebellum in motor control has been investigated extensively, but its contribution to the muscle pattern organization underlying goal-directed movements is still not fully understood. Muscle synergies may be used to characterize multimuscle pattern organization irrespective of time (spatial synergies), in time irrespective of the muscles (temporal synergies), and both across muscles and in time (spatiotemporal synergies). The decomposition of muscle patterns as combinations of different types of muscle synergies offers the possibility to identify specific changes due to neurological lesions. In this study, we recorded electromyographic activity from 13 shoulder and arm muscles in subjects with cerebellar ataxias (CA) and in age-matched healthy subjects (HS) while they performed reaching movements in multiple directions. We assessed whether cerebellar damage affects the organization of muscle patterns by extracting different types of muscle synergies from the muscle patterns of each HS and using these synergies to reconstruct the muscle patterns of all other participants. We found that CA muscle patterns could be accurately captured only by spatial muscle synergies of HS. In contrast, there were significant differences in the reconstruction R² values for both spatiotemporal and temporal synergies, with an interaction between the two synergy types indicating a larger difference for spatiotemporal synergies. Moreover, the reconstruction quality using spatiotemporal synergies correlated with the severity of impairment. These results indicate that cerebellar damage affects the temporal and spatiotemporal organization, but not the spatial organization, of the muscle patterns, suggesting that the cerebellum plays a key role in shaping their spatiotemporal organization.NEW & NOTEWORTHY In recent studies, the decomposition of muscle activity patterns has revealed a modular organization of the motor commands. We show, for the first time, that muscle patterns of subjects with cerebellar damage share with healthy controls spatial, but not temporal and spatiotemporal, modules. Moreover, changes in spatiotemporal organization characterize the severity of the subject's impairment. These results suggest that the cerebellum has a specific role in shaping the spatiotemporal organization of the muscle patterns.

A whole body characterization of individual strategies, gender differences, and common styles in overarm throwing

Overarm throwing is a fundamental human skill. Since paleolithic hunter-gatherer societies, the ability of throwing played a key role in brain and body co-evolution. For decades, throwing skill acquisition has been the subject of developmental and gender studies. However, due to its complex multijoint nature, whole body throwing has found little space in quantitative studies of motor behavior. In this study we examined how overarm throwing varies within and between individuals in a sample of untrained adults. To quantitatively compare whole body kinematics across throwing actions, we introduced a new combination of spatiotemporal principal component, linear discrimination, and clustering analyses. We found that the identity and gender of a thrower can be robustly inferred by the kinematics of a single throw, reflecting the characteristic features in individual throwing strategies and providing a quantitative ground for the well-known differences between males and females in throwing behavior. We also identified four main classes of throwing strategies, stable within individuals and resembling the main stages of throwing proficiency acquisition during motor development. These results support earlier proposals linking interindividual and gender differences in throwing, with skill acquisition interrupted at different stages of the typical developmental trajectory of throwing motor behavior.NEW & NOTEWORTHY Unconstrained throwing, because of its complexity, received little attention in quantitative motor control studies. By introducing a new approach to analyze whole body kinematics, we quantitatively characterized gender effects, interindividual differences, and common patterns in nontrained throwers. The four throwing styles identified across individuals resemble different stages in the acquisition of throwing skills during development. These results advance our understanding of complex motor skills, bridging the gap between motor control, motor development, and sport science.

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.

Fixed muscle synergies and their potential to improve the intuitive control of myoelectric assistive technology for upper extremities

BACKGROUND

Users of myoelectric controlled assistive technology (AT) for upper extremities experience difficulties in controlling this technology in daily life, partly because the control is non-intuitive. Making the control of myoelectric AT intuitive may resolve the experienced difficulties. The present paper was inspired by the suggestion that intuitive control may be achieved if the control of myoelectric AT is based on neuromotor control principles. A significant approach within neurocomputational motor control suggests that myosignals are produced via a limited number of fixed muscle synergies. To effectively employ this approach in myoelectric AT, it is required that a limited number of muscle synergies is systematically exploited, also when muscles are used differently as required in controlling myoelectric AT. Therefore, the present study examined the systematic exploitation of muscle synergies when muscles were used differently to complete point-to-point movements with and without a rod.

METHODS

Healthy participants made multidirectional point-to-point movements with different end-effectors, i.e. with the index finger and with rods of different lengths. Myosignals were collected from 22 muscles in the arm, trunk, and back, and subsequently partitioned into muscle synergies per end-effector and for a pooled dataset including all end-effectors. The exploitation of these muscle synergies was assessed by evaluating the similarity of structure and explanatory ability of myosignals of per end-effector muscle synergies and the contribution of pooled muscle synergies across end-effectors.

RESULTS

Per end-effector, 3-5 muscle synergies could explain 73.8-81.1% of myosignal variation, whereas 6-8 muscle synergies from the pooled dataset also captured this amount of myosignal variation. Subsequent analyses showed that gradually different muscle synergies-extracted from separate end-effectors-were exploited across end-effectors. In line with this result, the order of contribution of muscle synergies extracted from the pooled dataset gradually reversed across end-effectors.

CONCLUSION

A limited number of muscle synergies was systematically exploited in the examined set of movements, indicating a potential for the fixed muscle synergy approach to improve the intuitive control of myoelectric AT. Given the gradual change in muscle synergy exploitation across end-effectors, future research should examine whether this potential can be extended to a larger range of movements and tasks.

Dynamic Musculoskeletal Functional Morphology: Integrating diceCT and XROMM

The tradeoff between force and velocity in skeletal muscle is a fundamental constraint on vertebrate musculoskeletal design (form:function relationships). Understanding how and why different lineages address this biomechanical problem is an important goal of vertebrate musculoskeletal functional morphology. Our ability to answer questions about the different solutions to this tradeoff has been significantly improved by recent advances in techniques for quantifying musculoskeletal morphology and movement. Herein, we have three objectives: (1) review the morphological and physiological parameters that affect muscle function and how these parameters interact; (2) discuss the necessity of integrating morphological and physiological lines of evidence to understand muscle function and the new, high resolution imaging technologies that do so; and (3) present a method that integrates high spatiotemporal resolution motion capture (XROMM, including its corollary fluoromicrometry), high resolution soft tissue imaging (diceCT), and electromyography to study musculoskeletal dynamics in vivo. The method is demonstrated using a case study of in vivo primate hyolingual biomechanics during chewing and swallowing. A sensitivity analysis demonstrates that small deviations in reconstructed hyoid muscle attachment site location introduce an average error of 13.2% to in vivo muscle kinematics. The observed hyoid and muscle kinematics suggest that hyoid elevation is produced by multiple muscles and that fascicle rotation and tendon strain decouple fascicle strain from hyoid movement and whole muscle length. Lastly, we highlight current limitations of these techniques, some of which will likely soon be overcome through methodological improvements, and some of which are inherent. Anat Rec, 301:378-406, 2018. © 2018 Wiley Periodicals, Inc.

Where Are You Throwing the Ball? I Better Watch Your Body, Not Just Your Arm!

The ability to intercept or avoid a moving object, whether to catch a ball, snatch one's prey, or avoid the path of a predator, is a skill that has been acquired throughout evolution by many species in the animal kingdom. This requires processing early visual cues in order to program anticipatory motor responses tuned to the forthcoming event. Here, we explore the nature of the early kinematics cues that could inform an observer about the future direction of a ball projected with an unconstrained overarm throw. Our goal was to pinpoint the body segments that, throughout the temporal course of the throwing action, could provide key cues for accurately predicting the side of the outgoing ball. We recorded whole-body kinematics from twenty non-expert participants performing unconstrained overarm throws at four different targets placed on a vertical plane at 6 m distance. In order to characterize the spatiotemporal structure of the information embedded in the kinematics of the throwing action about the outgoing ball direction, we introduced a novel combination of dimensionality reduction and machine learning techniques. The recorded kinematics clearly shows that throwing styles differed considerably across individuals, with corresponding inter-individual differences in the spatio-temporal structure of the thrower predictability. We found that for most participants it is possible to predict the region where the ball hit the target plane, with an accuracy above 80%, as early as 400-500 ms before ball release. Interestingly, the body parts that provided the most informative cues about the action outcome varied with the throwing style and during the time course of the throwing action. Not surprisingly, at the very end of the action, the throwing arm is the most informative body segment. However, cues allowing for predictions to be made earlier than 200 ms before release are typically associated to other body parts, such as the lower limbs and the contralateral arm. These findings are discussed in the context of the sport-science literature on throwing and catching interactive tasks, as well as from the wider perspective of the role of sensorimotor coupling in interpersonal social interactions.

Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate postural and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the postural and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to define static and phasic components of muscle activity using muscle torques.

Empirical Evaluation of Voluntarily Activatable Muscle Synergies

The muscle synergy hypothesis assumes that individual muscle synergies are independent of each other and voluntarily controllable. However, this assumption has not been empirically tested. This study tested if human subjects can voluntarily activate individual muscle synergies extracted by non-negative matrix factorization (NMF), the standard mathematical method for synergy extraction. We defined the activation of a single muscle synergy as the generation of a muscle activity pattern vector parallel to the single muscle synergy vector. Subjects performed an isometric force production task with their right hand, and the 13 muscle activity patterns associated with their elbow and shoulder movements were measured. We extracted muscle synergies during the task using electromyogram (EMG) data and the NMF method with varied numbers of muscle synergies. The number (N) of muscle synergies was determined by using the variability accounted for (VAF, N_VAF) and the coefficient of determination (CD, N_CD). An additional muscle synergy model with N_AD was also considered. We defined a conventional muscle synergy as the muscle synergy extracted by the N_VAF, N_CD, and N_AD. We also defined an extended muscle synergy as the muscle synergy extracted by the N_EX> N_AD. To examine whether the individual muscle synergy was voluntarily activatable or not, we calculated the index of independent activation, which reflects similarities between a selected single muscle synergy and the current muscle activation pattern of the subject. Subjects were visually feed-backed the index of independent activation, then instructed to generate muscle activity patterns similar to the conventional and extended muscle synergies. As a result, an average of 90.8% of the muscle synergy extracted by the N_VAF was independently activated. However, the proportion of activatable muscle synergies extracted by N_CD and N_AD was lower. These results partly support the assumption of the muscle synergy hypothesis, i.e., that the conventional method can extract voluntarily and independently activatable muscle synergies by using the appropriate index of reconstruction. Moreover, an average of 25.5% of the extended muscle synergy was significantly activatable. This result suggests that the CNS can use extended muscle synergies to perform voluntary movements.

Multidigit force control during unconstrained grasping in response to object perturbations

Because of the complex anatomy of the human hand, in the absence of external constraints, a large number of postures and force combinations can be used to attain a stable grasp. Motor synergies provide a viable strategy to solve this problem of motor redundancy. In this study, we exploited the technical advantages of an innovative sensorized object to study unconstrained hand grasping within the theoretical framework of motor synergies. Participants were required to grasp, lift, and hold the sensorized object. During the holding phase, we repetitively applied external disturbance forces and torques and recorded the spatiotemporal distribution of grip forces produced by each digit. We found that the time to reach the maximum grip force during each perturbation was roughly equal across fingers, consistent with a synchronous, synergistic stiffening across digits. We further evaluated this hypothesis by comparing the force distribution of human grasping vs. robotic grasping, where the control strategy was set by the experimenter. We controlled the global hand stiffness of the robotic hand and found that this control algorithm produced a force pattern qualitatively similar to human grasping performance. Our results suggest that the nervous system uses a default whole hand synergistic control to maintain a stable grasp regardless of the number of digits involved in the task, their position on the objects, and the type and frequency of external perturbations.NEW & NOTEWORTHY We studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistently with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.

Comparison of muscle synergies for running between different foot strike patterns

It is well known that humans run with a fore-foot strike (FFS), a mid-foot strike (MFS) or a rear-foot strike (RFS). A modular neural control mechanism of human walking and running has been discussed in terms of muscle synergies. However, the neural control mechanisms for different foot strike patterns during running have been overlooked even though kinetic and kinematic differences between different foot strike patterns have been reported. Thus, we examined the differences in the neural control mechanisms of human running between FFS and RFS by comparing the muscle synergies extracted from each foot strike pattern during running. Muscle synergies were extracted using non-negative matrix factorization with electromyogram activity recorded bilaterally from 12 limb and trunk muscles in ten male subjects during FFS and RFS running at different speeds (5-15 km/h). Six muscle synergies were extracted from all conditions, and each synergy had a specific function and a single main peak of activity in a cycle. The six muscle synergies were similar between FFS and RFS as well as across subjects and speeds. However, some muscle weightings showed significant differences between FFS and RFS, especially the weightings of the tibialis anterior of the landing leg in synergies activated just before touchdown. The activation patterns of the synergies were also different for each foot strike pattern in terms of the timing, duration, and magnitude of the main peak of activity. These results suggest that the central nervous system controls running by sending a sequence of signals to six muscle synergies. Furthermore, a change in the foot strike pattern is accomplished by modulating the timing, duration and magnitude of the muscle synergy activity and by selectively activating other muscle synergies or subsets of the muscle synergies.

Direction-dependent arm kinematics reveal optimal integration of gravity cues

The brain has evolved an internal model of gravity to cope with life in the Earth's gravitational environment. How this internal model benefits the implementation of skilled movement has remained unsolved. One prevailing theory has assumed that this internal model is used to compensate for gravity's mechanical effects on the body, such as to maintain invariant motor trajectories. Alternatively, gravity force could be used purposely and efficiently for the planning and execution of voluntary movements, thereby resulting in direction-depending kinematics. Here we experimentally interrogate these two hypotheses by measuring arm kinematics while varying movement direction in normal and zero-G gravity conditions. By comparing experimental results with model predictions, we show that the brain uses the internal model to implement control policies that take advantage of gravity to minimize movement effort.

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.

Grasp-Based Functional Coupling Between Reach- and Grasp-Related Components of Forelimb Muscle Activity

How are appropriate combinations of forelimb muscles selected during reach-to-grasp movements in the presence of neuromotor redundancy and important task-related constraints? The authors tested whether grasp type or target location preferentially influence the selection and synergistic coupling between forelimb muscles during reach-to-grasp movements. Factor analysis applied to 14-20 forelimb electromyograms recorded from monkeys performing reach-to-grasp tasks revealed 4-6 muscle components that showed transport/preshape- or grasp-related features. Weighting coefficients of transport/preshape-related components demonstrated strongest similarities for reaches that shared the same grasp type rather than the same target location. Scaling coefficients of transport/preshape- and grasp-related components showed invariant temporal coupling. Thus, grasp type influenced strongly both transport/preshape- and grasp-related muscle components, giving rise to grasp-based functional coupling between forelimb muscles.

Mapping Muscles Activation to Force Perception during Unloading

It has been largely proved that while judging a force humans mainly rely on the motor commands produced to interact with that force (i.e., sense of effort). Despite of a large bulk of previous investigations interested in understanding the contributions of the descending and ascending signals in force perception, very few attempts have been made to link a measure of neural output (i.e., EMG) to the psychophysical performance. Indeed, the amount of correlation between EMG activity and perceptual decisions can be interpreted as an estimate of the contribution of central signals involved in the sensation of force. In this study we investigated this correlation by measuring the muscular activity of eight arm muscles while participants performed a quasi-isometric force detection task. Here we showed a method to quantitatively describe muscular activity ("muscle-metric function") that was directly comparable to the description of the participants' psychophysical decisions about the stimulus force. We observed that under our experimental conditions, muscle-metric absolute thresholds and the shape of the muscle-metric curves were closely related to those provided by the psychophysics. In fact a global measure of the muscles considered was able to predict approximately 60% of the perceptual decisions total variance. Moreover the inter-subjects differences in psychophysical sensitivity showed high correlation with both participants' muscles sensitivity and participants' joint torques. Overall, our findings gave insights into both the role played by the corticospinal motor commands while performing a force detection task and the influence of the gravitational muscular torque on the estimation of vertical forces.

Modularity for Motor Control and Motor Learning

How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.

Task-discriminative space-by-time factorization of muscle activity

Movement generation has been hypothesized to rely on a modular organization of muscle activity. Crucial to this hypothesis is the ability to perform reliably a variety of motor tasks by recruiting a limited set of modules and combining them in a task-dependent manner. Thus far, existing algorithms that extract putative modules of muscle activations, such as Non-negative Matrix Factorization (NMF), identify modular decompositions that maximize the reconstruction of the recorded EMG data. Typically, the functional role of the decompositions, i.e., task accomplishment, is only assessed a posteriori. However, as motor actions are defined in task space, we suggest that motor modules should be computed in task space too. In this study, we propose a new module extraction algorithm, named DsNM3F, that uses task information during the module identification process. DsNM3F extends our previous space-by-time decomposition method (the so-called sNM3F algorithm, which could assess task performance only after having computed modules) to identify modules gauging between two complementary objectives: reconstruction of the original data and reliable discrimination of the performed tasks. We show that DsNM3F recovers the task dependence of module activations more accurately than sNM3F. We also apply it to electromyographic signals recorded during performance of a variety of arm pointing tasks and identify spatial and temporal modules of muscle activity that are highly consistent with previous studies. DsNM3F achieves perfect task categorization without significant loss in data approximation when task information is available and generalizes as well as sNM3F when applied to new data. These findings suggest that the space-by-time decomposition of muscle activity finds robust task-discriminating modular representations of muscle activity and that the insertion of task discrimination objectives is useful for describing the task modulation of module recruitment.

A novel computational framework for deducing muscle synergies from experimental joint moments

Prior experimental studies have hypothesized the existence of a "muscle synergy" based control scheme for producing limb movements and locomotion in vertebrates. Such synergies have been suggested to consist of fixed muscle grouping schemes with the co-activation of all muscles in a synergy resulting in limb movement. Quantitative representations of these groupings (termed muscle weightings) and their control signals (termed synergy controls) have traditionally been derived by the factorization of experimentally measured EMG. This study presents a novel approach for deducing these weightings and controls from inverse dynamic joint moments that are computed from an alternative set of experimental measurements-movement kinematics and kinetics. This technique was applied to joint moments for healthy human walking at 0.7 and 1.7 m/s, and two sets of "simulated" synergies were computed based on two different criteria (1) synergies were required to minimize errors between experimental and simulated joint moments in a musculoskeletal model (pure-synergy solution) (2) along with minimizing joint moment errors, synergies also minimized muscle activation levels (optimal-synergy solution). On comparing the two solutions, it was observed that the introduction of optimality requirements (optimal-synergy) to a control strategy solely aimed at reproducing the joint moments (pure-synergy) did not necessitate major changes in the muscle grouping within synergies or the temporal profiles of synergy control signals. Synergies from both the simulated solutions exhibited many similarities to EMG derived synergies from a previously published study, thus implying that the analysis of the two different types of experimental data reveals similar, underlying synergy structures.