An approach for improving repeatability and reliability of non-negative matrix factorization for muscle synergy analysis

Stabilizing leaning postures with feedback controlled functional neuromuscular stimulation after trunk paralysis

Spinal cord injury (SCI) can cause paralysis of trunk and hip musculature that negatively impacts seated balance and ability to lean away from an upright posture and interact fully with the environment. Constant levels of electrical stimulation of peripheral nerves can activate typically paralyzed muscles and aid in maintaining a single upright seated posture. However, in the absence of a feedback controller, such seated postures and leaning motions are inherently unstable and unable to respond to perturbations. Three individuals with motor complete SCI who had previously received a neuroprosthesis capable of activating the hip and trunk musculature volunteered for this study. Subject-specific muscle synergies were identified through system identification of the lumbar moments produced via neural stimulation. Synergy-based calculations determined the real-time stimulation parameters required to assume leaning postures. When combined with a proportional, integral, derivative (PID) feedback controller and an accelerometer to infer trunk orientation, all individuals were able to assume non-erect postures of 30-40° flexion and 15° lateral bending. Leaning postures increased forward reaching capabilities by 10.2, 46.7, and 16 cm respectively for each subject when compared with no stimulation. Additionally, the leaning controllers were able to resist perturbations of up to 90 N, and all subjects perceived the leaning postures as moderately to very stable. Implementation of leaning controllers for neuroprostheses have the potential of expanding workspaces, increasing independence, and facilitating activities of daily living for individuals with paralysis.

Synergy-based functional electrical stimulation and robotic-assisted for retraining reach-to-grasp in stroke: a study protocol for a randomized controlled trial

BACKGROUND

Stroke survivors have long-term upper limb impairment, which impacts the quality of life (QOL) and social reintegration, but there is lack of effective therapeutic strategies and novel technologies. Customized multi-muscle functional electrical stimulation (FES) based on the muscle synergy of healthy adults and robotic-assisted therapy (RAT) have been proved efficacy respectively. Synergy-based FES combined with RAT can be a novel and more effective therapy for upper limb recovery of stroke survivors from the perspective of synergistic enhancement. However, few studies have examined the effectiveness of combined synergy-based FES and RAT, especially for motor control evaluated by reach-to-grasp (RTG) movements. The main objective of the following research protocol is to evaluate the effectiveness and efficacy, as well as adoptability, of FES-RAT and FES or RAT rehabilitation program for upper limb function improvement after stroke.

METHODS

This will be an assessor-blinded randomized controlled trial involving a 12-week intervention and a 6-month follow-up. Stratified randomization will be used to equally and randomly assign 162 stroke patients into the FES + conventional rehabilitation program (CRP) group, RAT + CRP group and FES-RAT + CRP group. Interventions will be provided in 5 sessions per week, with a total of 60 sessions. The primary outcome measurements will include the Fugl-Meyer Assessment and Biomechanical Assessment of RTG movements. The secondary outcome measurements will include quality of life and brain neuroplasticity assessments by MRI. Evaluations will be performed at five time points, including at baseline, 6 weeks and 12 weeks from the start of treatment, and 3 months and 6 months following the end of treatment. A two-way analysis of variance with repeated measures will be applied to examine the main effects of the group, the time factor and group-time interaction effects.

DISCUSSION

The results of the study protocol will provide high quality evidence for integrated synergy-based FES and RAT, and synergy-based FES alone and guide the design of more effective treatment methods for stroke rehabilitation.

TRIAL REGISTRATION

ChiCTR2300071588.

Computational prediction of muscle synergy using a finite element framework for a musculoskeletal model on lower limb

Previous studies have demonstrated that the central nervous system activates muscles in module patterns to reduce the complexity needed to control each muscle while producing a movement, which is referred to as muscle synergy. In previous musculoskeletal modeling-based muscle synergy analysis studies, as a result of simplification of the joints, a conventional rigid-body link musculoskeletal model failed to represent the physiological interactions of muscle activation and joint kinematics. However, the interaction between the muscle level and joint level that exists in vivo is an important relationship that influences the biomechanics and neurophysiology of the musculoskeletal system. In the present, a lower limb musculoskeletal model coupling a detailed representation of a joint including complex contact behavior and material representations was used for muscle synergy analysis using a decomposition method of non-negative matrix factorization (NMF). The complexity of the representation of a joint in a musculoskeletal system allows for the investigation of the physiological interactions in vivo on the musculoskeletal system, thereby facilitating the decomposition of the muscle synergy. Results indicated that, the activities of the 20 muscles on the lower limb during the stance phase of gait could be controlled by three muscle synergies, and total variance accounted for by synergies was 86.42%. The characterization of muscle synergy and musculoskeletal biomechanics is consistent with the results, thus explaining the formational mechanism of lower limb motions during gait through the reduction of the dimensions of control issues by muscle synergy and the central nervous system.

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection

Introduction

The time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.

Methods

In this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.

Results

The detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.

Discussion

This study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction.

Heavier Load Alters Upper Limb Muscle Synergy with Correlated fNIRS Responses in BA4 and BA6

In neurorehabilitation, motor performances may improve if patients could accomplish the training by overcoming mechanical loads. When the load inertia is increased, it has been found to trigger linear responses in motor-related cortices. The cortical responses, however, are unclear whether they also correlate to changes in muscular patterns. Therefore, it remains difficult to justify the magnitude of load during rehabilitation because of the gap between cortical and muscular activation. Here, we test the hypothesis that increases in load inertia may alter the muscle synergies, and the change in synergy may correlate with cortical activation. Twelve healthy subjects participated in the study. Each subject lifted dumbbells (either 0, 3, or 15 pounds) from the resting position to the armpit repetitively at 1 Hz. Surface electromyographic signals were collected from 8 muscles around the shoulder and the elbow, and hemodynamic signals were collected using functional near-infrared spectroscopy from motor-related regions Brodmann Area 4 (BA4) and BA6. Results showed that, given higher inertia, the synergy vectors differed farther from the baseline. Moreover, synergy similarity on the vector decreased linearly with cortical responses in BA4 and BA6, which associated with increases in inertia. Despite studies in literature that movements with similar kinematics tend not to differ in synergy vectors, we show a different possibility that the synergy vectors may deviate from a baseline. At least 2 consequences of adding inertia have been identified: to decrease synergy similarity and to increase motor cortical activity. The dual effects potentially provide a new benchmark for therapeutic goal setting.

A computational method for estimating trunk muscle activations during gait using lower extremity muscle synergies

One of the surgical treatments for pelvic sarcoma is the restoration of hip function with a custom pelvic prosthesis after cancerous tumor removal. The orthopedic oncologist and orthopedic implant company must make numerous often subjective decisions regarding the design of the pelvic surgery and custom pelvic prosthesis. Using personalized musculoskeletal computer models to predict post-surgery walking function and custom pelvic prosthesis loading is an emerging method for making surgical and custom prosthesis design decisions in a more objective manner. Such predictions would necessitate the estimation of forces generated by muscles spanning the lower trunk and all joints of the lower extremities. However, estimating trunk and leg muscle forces simultaneously during walking based on electromyography (EMG) data remains challenging due to the limited number of EMG channels typically used for measurement of leg muscle activity. This study developed a computational method for estimating unmeasured trunk muscle activations during walking using lower extremity muscle synergies. To facilitate the calibration of an EMG-driven model and the estimation of leg muscle activations, EMG data were collected from each leg. Using non-negative matrix factorization, muscle synergies were extracted from activations of leg muscles. On the basis of previous studies, it was hypothesized that the time-varying synergy activations were shared between the trunk and leg muscles. The synergy weights required to reconstruct the trunk muscle activations were determined through optimization. The accuracy of the synergy-based method was dependent on the number of synergies and optimization formulation. With seven synergies and an increased level of activation minimization, the estimated activations of the erector spinae were strongly correlated with their measured activity. This study created a custom full-body model by combining two existing musculoskeletal models. The model was further modified and heavily personalized to represent various aspects of the pelvic sarcoma patient, all of which contributed to the estimation of trunk muscle activations. This proposed method can facilitate the prediction of post-surgery walking function and pelvic prosthesis loading, as well as provide objective evaluations for surgical and prosthesis design decisions.

Data Fusion-Based Musculoskeletal Synergies in the Grasping Hand

The hypothesis that the central nervous system (CNS) makes use of synergies or movement primitives in achieving simple to complex movements has inspired the investigation of different types of synergies. Kinematic and muscle synergies have been extensively studied in the literature, but only a few studies have compared and combined both types of synergies during the control and coordination of the human hand. In this paper, synergies were extracted first independently (called kinematic and muscle synergies) and then combined through data fusion (called musculoskeletal synergies) from 26 activities of daily living in 22 individuals using principal component analysis (PCA) and independent component analysis (ICA). By a weighted linear combination of musculoskeletal synergies, the recorded kinematics and the recorded muscle activities were reconstructed. The performances of musculoskeletal synergies in reconstructing the movements were compared to the synergies reported previously in the literature by us and others. The results indicate that the musculoskeletal synergies performed better than the synergies extracted without fusion. We attribute this improvement in performance to the musculoskeletal synergies that were generated on the basis of the cross-information between muscle and kinematic activities. Moreover, the synergies extracted using ICA performed better than the synergies extracted using PCA. These musculoskeletal synergies can possibly improve the capabilities of the current methodologies used to control high dimensional prosthetics and exoskeletons.

A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.

A fair and EMG-validated comparison of recruitment criteria, musculotendon models and muscle coordination strategies, for the inverse-dynamics based optimization of muscle forces during gait

Experimental studies and EMG collections suggest that a specific strategy of muscle coordination is chosen by the central nervous system to perform a given motor task. A popular mathematical approach for solving the muscle recruitment problem is optimization. Optimization-based methods minimize or maximize some criterion (objective function or cost function) which reflects the mechanism used by the central nervous system to recruit muscles for the movement considered. The proper cost function is not known a priori, so the adequacy of the chosen function must be validated according to the obtained results. In addition of the many criteria proposed, several physiological representations of the musculotendon actuator dynamics (that prescribe constraints for the forces) along with different musculoskeletal models can be found in the literature, which hinders the selection of the best neuromusculotendon model for each application. Seeking to provide a fair base for comparison, this study measures the efficiency and accuracy of: (i) four different criteria within the static optimization approach (where the physiological character of the muscle, which affects the constraints of the forces, is not considered); (ii) three physiological representations of the musculotendon actuator dynamics: activation dynamics with elastic tendon, simplified activation dynamics with rigid tendon and rigid tendon without activation dynamics; (iii) a synergy-based method; all of them within the framework of inverse-dynamics based optimization. Motion/force/EMG gait analyses were performed on ten 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, musculotendon kinematics and moment arms. Muscle activations were then estimated using the different approaches, and these estimates were compared with EMG measurements. Although no significant differences were obtained with all the methods at statistical level, it must be pointed out that a higher complexity of the method does not guarantee better results, as the best correlations with experimental values were obtained with two simplified approaches: the static optimization and the physiological approach with simplified activation dynamics and rigid tendon, both using the sum of the squares of muscle forces as objective function.

Functional muscle synergies to support the knee against moment specific loads while weight bearing

OBJECTIVE

Externally applied abduction and rotational loads are major contributors to the knee joint injury mechanism; yet, how muscles work together to stabilize the knee against these loads remains unclear. Our study sought to evaluate lower limb functional muscle synergies in healthy young adults such that muscle activation can be directly related to internal knee joint moments.

METHODS

Concatenated non-negative matrix factorization extracted muscle and moment synergies of 22 participants from electromyographic signals and joint moments elicited during a weight-bearing force matching protocol.

RESULTS

Two synergy sets were extracted: Set 1 included four synergies, each corresponding to a general anterior, posterior, medial, or lateral force direction. Frontal and transverse moments were coupled during medial and lateral force directions. Set 2 included six synergies, each corresponding to a moment type (extension/flexion, ab/adduction, internal/external rotation). Hamstrings and quadriceps dominated synergies associated with respective flexion and extension moments while quadriceps-hamstring co-activation was associated with knee abduction. Rotation moments were associated with notable contributions from hamstrings, quadriceps, gastrocnemius, and hip ab/adductors, corresponding to a general co-activation muscle synergy.

CONCLUSION

Our results highlight the importance of muscular co-activation of all muscles crossing the knee to support it during injury-inducing loading conditions such as externally applied knee abduction and rotation. Functional muscle synergies can provide new insight into the relationship between neuromuscular control and knee joint stability by directly associating biomechanical variables to muscle activation.

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.

Spatial filtering for enhanced high-density surface electromyographic examination of neuromuscular changes and its application to spinal cord injury

Background

Spatial filtering of multi-channel signals is considered to be an effective pre-processing approach for improving signal-to-noise ratio. The use of spatial filtering for preprocessing high-density (HD) surface electromyogram (sEMG) helps to extract critical spatial information, but its application to non-invasive examination of neuromuscular changes have not been well investigated.

Methods

Aimed at evaluating how spatial filtering can facilitate examination of muscle paralysis, three different spatial filtering methods are presented using principle component analysis (PCA) algorithm, non-negative matrix factorization (NMF) algorithm, and both combination, respectively. Their performance was evaluated in terms of diagnostic power, through HD-sEMG clustering index (CI) analysis of neuromuscular changes in paralyzed muscles following spinal cord injury (SCI).

Results

The experimental results showed that: (1) The CI analysis of conventional single-channel sEMG can reveal complex neuromuscular changes in paralyzed muscles following SCI, and its diagnostic power has been confirmed to be characterized by the variance of Z scores; (2) the diagnostic power was highly dependent on the location of sEMG recording channel. Directly averaging the CI diagnostic indicators over channels just reached a medium level of the diagnostic power; (3) the use of either PCA-based or NMF-based filtering method yielded a greater diagnostic power, and their combination could even enhance the diagnostic power significantly.

Conclusions

This study not only presents an essential preprocessing approach for improving diagnostic power of HD-sEMG, but also helps to develop a standard sEMG preprocessing pipeline, thus promoting its widespread application.

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.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes' performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes' performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment.

Muscle Synergies in Parkinson's Disease

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

Differences in muscle synergies between healthy subjects and transfemoral amputees during normal transient-state walking speed

BACKGROUND

Lower limb amputation is a major public health issue globally, and its prevalence is increasing significantly around the world. Previous studies on lower limb amputees showed analogous complexity implemented by the neurological system which does not depend on the level of amputation.

RESEARCH QUESTION

What are the differences in muscle synergies between healthy subjects (HS) and transfemoral amputees (TFA) during self-selected normal transient-state walking speed?

METHODS

thirteen male HS and eleven male TFA participated in this study. Surface electromyography (sEMG) data were collected from HS dominant leg and TFA intact limb. Concatenated non-negative matrix factorization (CNMF) was used to extract muscle synergy components synergy vectors (S) and activation coefficient profiles (C). Correlation between a pair of synergy vectors from HS and TFA was analyzed by means of the coefficient of determination (R²). Statistical parametric mapping (SPM) was used to compare the temporal components of the muscle synergies between groups.

RESULTS

the highest correlation was perceived in synergy 2 (S2) and 3 (S3) and the lowest in synergy 1 (S1) and 4 (S4) between HS and TFA. Statistically significant differences were observed in all of the activation coefficients, particularly during the stance phase. Significant lag in the activation coefficient of S2 (due mainly to activated plantarflexors) resulted in a statistically larger portion of the gait cycle (GC) in stance phase in TFA.

SIGNIFICANCE

Understanding the activation patterns of lower limb amputees' muscles that control their intact leg (IL) and prosthetic leg (PL) joints could lead to greater knowledge of neuromuscular compensation strategies in amputees. Studying the low-dimensional muscle synergy patterns in the lower limbs can further this understanding. The findings in this study could contribute to improving gait rehabilitation of lower limb amputees and development of the new generation of prostheses.

Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post-modified non-negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking

Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed "synergy optimization," or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2-6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability.

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom. Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model. Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces. Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces. Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.

A Synergy-Based Motor Control Framework for the Fast Feedback Control of Musculoskeletal Systems

This paper presents a computational framework for the fast feedback control of musculoskeletal systems using muscle synergies. The proposed motor control framework has a hierarchical structure. A feedback controller at the higher level of hierarchy handles the trajectory planning and error compensation in the task space. This high-level task space controller only deals with the task-related kinematic variables, and thus is computationally efficient. The output of the task space controller is a force vector in the task space, which is fed to the low-level controller to be translated into muscle activity commands. Muscle synergies are employed to make this force-to-activation (F2A) mapping computationally efficient. The explicit relationship between the muscle synergies and task space forces allows for the fast estimation of muscle activations that result in the reference force. The synergy-enabled F2A mapping replaces a computationally heavy nonlinear optimization process by a vector decomposition problem that is solvable in real time. The estimation performance of the F2A mapping is evaluated by comparing the F2A-estimated muscle activities against the measured electromyography (EMG) data. The results show that the F2A algorithm can estimate the muscle activations using only the task-related kinematics/dynamics information with ∼70% accuracy. An example predictive simulation is also presented, and the results show that this feedback motor control framework can control arbitrary movements of a three-dimensional (3D) musculoskeletal arm model quickly and near optimally. It is two orders-of-magnitude faster than the optimal controller, with only 12% increase in muscle activities compared to the optimal. The developed motor control model can be used for real-time near-optimal predictive control of musculoskeletal system dynamics.

Using Muscle Synergy to Evaluate the Neck Muscular Activities during Normal Swallowing

Swallowing is an extremely complex motion controlled by multiple muscles on the front neck region. Normal swallowing is dependent on orderly activation and co-coordination of the associated neck muscles, known as muscle synergy. However, evidence for muscle synergy during normal swallowing is rarely investigated. In this study, we studied the muscle synergy associated with swallowing saliva based on high-density (HD) surface electromyography (sEMG) signals acquired from four healthy subjects. The non-negative matrix factorization algorithm was applied to reconstruct the muscle activation patterns, and the values of variance accounted for (VAF) coefficients were computed to determine the number of muscle synergies. The results showed that the VAF values raised with the increase in the number of synergies on both the left and right sides of the neck. And the variation tendency of the VAF values was almost similar between the left and right area with a significant correlation ($\text{r}=0.9902 \pm 0.0046$, $\mathrm {p}<0.05)$. Furthermore, it was observed that an average of 5 muscle synergies was the minimum number required to sufficiently reconstruct the spatial characteristics of the synergism between both sides of the neck. These results suggest that the muscle synergy approach could serve as a promising candidate to evaluate the muscular co-contractions during swallowing, and it might be a useful method for dysphagia monitoring and diagnoses.

The influence of musculoskeletal pain disorders on muscle synergies-A systematic review

Background

Musculoskeletal (MSK) pain disorders represent a group of highly prevalent and often disabling conditions. Investigating the structure of motor variability in response to pain may reveal novel motor impairment mechanisms that may lead to enhanced management of motor dysfunction associated with MSK pain disorders. This review aims to systematically synthesize the evidence on the influence of MSK pain disorders on muscle synergies.

Methods

Nine electronic databases were searched using Medical Subject Headings and keywords describing pain, electromyography and synergies. Relevant characteristics of included studies were extracted and assessed for generalizability and risk of bias. Due to the significant heterogeneity, a qualitative synthesis of the results was performed.

Results

The search resulted in a total of 1312 hits, of which seven articles were deemed eligible. There was unclear consistency that pain reduced the number of muscle synergies. There were low consistencies of evidence that the synergy vector (W weights) and activation coefficient (C weights) differed in painful compared to asymptomatic conditions. There was a high consistency that muscle synergies were dissimilar between painful and asymptomatic conditions.

Conclusions

MSK pain alters the structure of variability in muscle control, although its specific nature remains unclear. Greater consistency in muscle synergy analysis may be achieved with appropriate selection of muscles assessed and ensuring consistent achievement of motor task outcomes. Synergy analysis is a promising method to reveal novel understandings of altered motor control, which may facilitate the assessment and treatment of MSK pain disorders.

Influence of proximal trunk borne load on lower limb countermovement joint dynamics

Vertical jumping involves coordinating the temporal sequencing of angular motion, moment, and power across multiple joints. Studying the biomechanical coordination strategies that differentiates loaded from unloaded vertical jumping may better inform training prescription for athletes needing to jump with load. Common multivariate methods (e.g. Principal Components Analysis) cannot quantify coordination in a dataset with more than two modes. This study aimed to identify coordinative factors across four modes of variation using Parallel Factor (Parafac2) analysis, which may differentiate unloaded (body weight [BW]) from loaded (BW + 20% BW) countermovement jump (CMJ). Thirty-one participants performed unloaded and loaded CMJ. Three-dimensional motion capture with force plate analysis was performed. Inverse dynamics was used to quantify sagittal plane joint angle, velocity, moment, and joint power across the ankle, knee, and hip. The four-mode data were as follows: Mode A was jump cycle (101 cycle points), mode B was participant (31 participants by two load), mode C was joint (two sides by three joints), and mode D was variable (angle, velocity, moment, power). Three factors were extracted, which explained 95.1% of the data's variance. Only factors one (P = 0.001) and three (P < 0.001) significantly differentiated loaded from unloaded jumping. The body augmented hip-dominant at the start, and both hip and ankle dominant behaviors at the end of the ascending phase of the CMJ, but kept knee-dominant behavior invariant, when jumping with a 20% BW load. By studying the variation across all data modes, Parafac2 provides a holistic method of studying jumping coordination.

Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

Background: Synergistic recruitment of muscular activities is a generally accepted mechanism for motor function control, and motor dysfunction, such as cerebral palsy (CP), destroyed the synergistic electromyography activities of muscle group for limb movement. However, very little is known how motor dysfunction of CP affects the organization of the myoelectric frequency components due to the abnormal motor unit recruiting patterns. Objectives: Exploring whether the myoelectric activity can be represented with synergistic recruitment of surface electromyography (sEMG) frequency components; evaluating the effect of CP motor dysfunction on the synergistic recruitment of sEMG oscillations. Methods: Twelve CP infants and 17 typically developed (TD) infants are recruited for self-paced crawling on hands and knees. sEMG signals have been recorded from bilateral biceps brachii (BB) and triceps brachii (TB) muscles. Multi-scale oscillations are extracted via multivariate empirical mode decomposition (MEMD), and non-negative matrix factorization (NMF) method is employed to obtain synergistic pattern of these sEMG oscillations. The coefficient curve of sEMG oscillation synergies are adopted to quantify the time-varying recruitment of BB and TB myoelectric activity during infants crawling. Results: Three patterns of sEMG oscillation synergies with specific frequency ranges are extracted in BB and TB of CP or TD infants. The contribution of low-frequency oscillation synergy of BB in CP group is significantly less than that in TD group (p < 0.05) during forward swing phase for slow contraction; however, this low-frequency oscillation synergy keep higher level during the backward swing phase crawling. For the myoelectric activities of TB, there is not enough high-frequency oscillation recruitment of sEMG for the fast contraction in propulsive phase of CP infants crawling. Conclusion: Our results reveal that, the myoelectric activities of a muscle can be manifested as sEMG oscillation synergies, and motor dysfunction of CP degrade the synergistic recruitment of sEMG oscillations due to the impaired CNS regulation and destroyed MU/muscle fiber. Our preliminary work suggests that time-varying coefficient curve of sEMG oscillation synergies is a potential index to evaluate the abnormal recruitment of electromyography activities affected by CP disorders.

Assessment of ankle muscle activation by muscle synergies in healthy and post-stroke gait

OBJECTIVE

Impaired ankle dorsal and plantar flexor function is a frequent sequela of stroke. A better assessment of ankle muscle activation would be highly significant for stroke rehabilitation. The challenge in implementing current electromyography (EMG)-based assessments is due to problems with the variability and individuality of ankle muscle EMG profiles during walking. We have been studying a new technique using the muscle synergy method to quantify the characteristics that underlie ankle muscle activation to address this issue.

APPROACH

We processed surface EMG signals from ankle muscles and gait parameters collected from 20 healthy and 22 post-stroke subjects during walking. A non-negative matrix factorization algorithm was used to extract muscle synergies.

MAIN RESULTS

Our results suggest a featured muscle synergy structure (R  =  0.84, 95% CI: 0.83-0.85) underlying ankle muscle activation in both healthy and post-stroke subjects. The structure of the featured muscle synergy was robust in the same subjects across different conditions in the healthy group (R  =  0.97, 95% CI: 0.96-0.98) and the post-stroke group (R  =  0.95, 95% CI: 0.88-0.97). Compared to the stroke group, the synergy patterns of healthy subjects showed better regularity and higher inter-subject similarity (P  =  0.001). In addition, the results of muscle synergies were indicative of locomotor performance.

SIGNIFICANCE

The innovative quantitative results of this study can help us to better understand ankle muscle activation and will be a reference for clinical assessments and intervention studies.

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

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

Muscle synergy analysis in transtibial amputee during ramp ascending activity

In developed countries, the highest number of amputees are elderly with transtibial amputation. Walking on inclined surfaces is difficult for amputees due to loss of muscle volume and strength thereby transtibial amputees (TA) rely on the intact limb to maintain stability. The aim of this study was to use the concatenated non-negative matrix factorization (CNMF) technique to calculate muscle synergy components and compare the difference in muscle synergies and their associated activation profiles in the healthy and amputee groups during ramp ascending (RA) activity. Healthy subjects' dominant leg and amputee's intact leg (IL) were considered for recording surface electromyography (sEMG). The muscle synergies comparison showed a reasonable correlation between the healthy and amputee groups. This suggests the central nervous system (CNS) activates the same group of muscles synergistically. However, the activation coefficient profile (C) results indicated statistically significant difference (p <; 0.05) in some parts of the gait cycle (GC) in healthy and amputee groups. The difference exhibited in activation profiles of amputee's IL could be due to the instability of the prosthetic leg during the GC which resulted in alteration of the IL muscles activations. This information will be useful in rehabilitation and in the future development of prosthetic devices by using the IL muscles information to control the prostheses.