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

Analysis of muscle synergies and muscle network in sling exercise rehabilitation technique

The study assessed motor control strategies across the four sling exercises of supine sling exercise (SSE), prone sling exercise (PSE), left side-lying sling exercise (LLSE), and right side-lying sling exercise (RLSE) positions base on the muscle synergies and muscle network analyses. Muscle activities of bilateral transversus abdominis (TA), rectus abdominis, multifidus (MF), and erector spinae (ES) were captured via surface electromyography. Muscle synergies were extracted through principal components analysis (PCA) and non-negative matrix factorization (NNMF). Muscle synergies number, muscle synergies complexity, muscle synergies sparseness, muscle synergies clusters and muscle networks were calculated. PCA results indicated that SSE and PSE decomposed into 2.88 ± 0.20 and 2.82 ± 0.15 synergies respectively, while the LLSE and RLSE positions decomposed into 3.76 ± 0.14 and 3.71 ± 0.11 muscle synergies, respectively, which were more complex (P = 0.00) but less sparse (P = 0.01). Muscle synergies clusters analysis indicated common muscle synergies among different sling exercises. SSE position demonstrated specific muscle synergies with a strong contribution of the bilateral TA. LLSE-specific synergy has a strong contribution of the left erector spinae (ES). The RLSE-specific synergy has significant contributions from the right ES and multifidus. Muscle networks were functionally organized, with clustering coefficient (F(1.5, 24) = 6.041, P = 0.01) and global efficiency of the undirected network (F(1.5, 24) = 6.041, P = 0.01), and betweenness-centrality of the directed network (F(2.7, 44) = 6.453, P = 0.00). Our research highlights the importance of evaluating muscle synergies and network adaptation strategies in individuals with neuromuscular disorders and developing targeted therapeutic interventions accordingly.

Muscle synergies in upper limb stroke rehabilitation: a scoping review

INTRODUCTION

Upper limb impairment is a common consequence of stroke, significantly affecting the quality of life and independence of survivors. This scoping review assesses the emerging field of muscle synergy analysis in enhancing upper limb rehabilitation, focusing on the comparison of various methodologies and their outcomes. It aims to standardize these approaches to improve the effectiveness of rehabilitation interventions and drive future research in the domain.

EVIDENCE ACQUISITION

Studies included in this scoping review focused on the analysis of muscle synergies during longitudinal rehabilitation of stroke survivors' upper limbs. A systematic literature search was conducted using PubMed, Scopus, and Web of Science databases, until September 2023, and was guided by the PRISMA for scoping review framework.

EVIDENCE SYNTHESIS

Fourteen studies involving a total of 247 stroke patients were reviewed, featuring varied patient populations and rehabilitative interventions. Protocols differed among studies, with some utilizing robotic assistance and others relying on traditional therapy methods. Muscle synergy extraction was predominantly conducted using Non-Negative Matrix Factorization from electromyography data, focusing on key upper limb muscles essential for shoulder, elbow, and wrist rehabilitation. A notable observation across the studies was the heterogeneity in findings, particularly in the changes observed in the number, weightings, and temporal coefficients of muscle synergies. The studies indicated varied and complex relationships between muscle synergy variations and clinical outcomes. This diversity underscored the complexity involved in interpreting muscle coordination in the stroke population. The variability in results was also influenced by differing methodologies in muscle synergy analysis, highlighting a need for more standardized approaches to improve future research comparability and consistency.

CONCLUSIONS

The synthesis of evidence presented in this scoping review highlights the promising role of muscle synergy analysis as an indicator of motor control recovery in stroke rehabilitation. By offering a comprehensive overview of the current state of research and advocating for harmonized methodological practices in future longitudinal studies, this scoping review aspires to advance the field of upper limb rehabilitation, ensuring that post-stroke interventions are both scientifically grounded and optimally beneficial for patients.

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.

Biomechanical Analysis of Human Gait When Changing Velocity and Carried Loads: Simulation Study with OpenSim

Walking is one of the main activities of daily life and gait analysis can provide crucial data for the computation of biomechanics in many fields. In multiple applications, having reference data that include a variety of gait conditions could be useful for assessing walking performance. However, limited extensive reference data are available as many conditions cannot be easily tested experimentally. For this reason, a musculoskeletal model in OpenSim coupled with gait data (at seven different velocities) was used to simulate seven carried loads and all the combinations between the two parameters. The effects on lower limb biomechanics were measured with torque, power, and mechanical work. The results demonstrated that biomechanics was influenced by both speed and load. Our results expand the previous literature: in the majority of previous work, only a subset of the presented conditions was investigated. Moreover, our simulation approach provides comprehensive data that could be useful for applications in many areas, such as rehabilitation, orthopedics, medical care, and sports.

Quantitative evaluation of motion compensation in post-stroke rehabilitation training based on muscle synergy

Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes. Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body's neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients. Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

Influence of Backpack Carriage and Walking Speed on Muscle Synergies in Healthy Children

Four to five muscle synergies account for children's locomotion and appear to be consistent across alterations in speed and slopes. Backpack carriage induces alterations in gait kinematics in healthy children, raising questions regarding the clinical consequences related to orthopedic and neurological diseases and ergonomics. However, to support clinical decisions and characterize backpack carriage, muscle synergies can help with understanding the alterations induced in this condition at the motor control level. In this study, we investigated how children adjust the recruitment of motor patterns during locomotion, when greater muscular demands are required (backpack carriage). Twenty healthy male children underwent an instrumental gait analysis and muscle synergies extraction during three walking conditions: self-selected, fast and load conditions. In the fast condition, a reduction in the number of synergies (three to four) was needed for reconstructing the EMG signal with the same accuracy as in the other conditions (three to five). Synergies were grouped in only four clusters in the fast condition, while five clusters were needed for the self-selected condition. The right number of clusters was not clearly identified in the load condition. Speed and backpack carriage altered nearly every spatial-temporal parameter of gait, whereas kinematic alterations reflected mainly hip and pelvis adaptations. Although the synergistic patterns were consistent across conditions, indicating a similar motor pattern in different conditions, the fast condition required fewer synergies for reconstructing the EMG signal with the same level of accuracy.

Progressive unsupervised control of myoelectric upper limbs

Objective.Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.Approach.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.Main results.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.Significance.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Clinical Features to Predict the Use of a sEMG Wearable Device (REMO®) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO^®) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface. The aim of this study was to define the clinical features of stroke survivors able to perform ten, five, or no hand movements for rehabilitation training. 117 stroke patients were tested: 65% of patients were able to control ten movements, 19% of patients could control nine to one movement, and 16% could control no movements. Results indicated that mild upper limb motor impairment (Fugl-Meyer Upper Extremity ≥ 18 points) predicted the control of ten movements and no flexor carpi muscle spasticity predicted the control of five movements. Finally, severe impairment of upper limb motor function (Fugl-Meyer Upper Extremity > 10 points) combined with no pain and no restrictions of upper limb joints predicted the control of at least one movement. In conclusion, the residual motor function, pain and joints restriction, and spasticity at the upper limb are the most important clinical features to use for a wearable REMO^® for hand rehabilitation training.