Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control

In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms(DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions

Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking

The knowledge of muscle activation patterns when doing a certain task in subjects with anterior cruciate ligament deficiency could help to improve their rehabilitation treatment. The goal of this study is to identify differences in such patterns between anterior cruciate ligament–deficient and healthy subjects during walking. Methods Electromyographic data for eight muscles were measured in a sample of eighteen subjects with anterior cruciate ligament deficiency, in both injured (ipsilateral group) and non-injured (contralateral group) legs, and a sample of ten healthy subjects (control group). The analysis was carried out at two levels: activation-–deactivation patterns and muscle synergies. Muscle synergy components were calculated using a non-negative matrix factorization algorithm. Findings The results showed that there was a higher co-contraction in injured than in healthy subjects. Although all muscles were activated similarly since all subjects developed the same task (walking), some differences could be observed among the analyzed groups. Interpretation The observed differences in the synergy components of injured subjects suggested that those individuals alter muscle activation patterns to stabilize the knee joint. This analysis could provide valuable information for the physiotherapist to identify alterations in muscle activation patterns during the follow-up of the subject’s rehabilitation.Postprint (author's final draft

A model-based approach to predict muscle synergies using optimization: application to feedback control

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.The authors wish to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this study

A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals

Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke

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

Shared and Task-Specific Muscle Synergies during Normal Walking and Slipping

Falling accidents are costly due to their prevalence in the workplace. Slipping has been known to be the main cause of falling. Understanding the motor response used to regain balance after slipping is crucial to developing intervention strategies for effective recovery. Interestingly, studies on spinalized animals and studies on animals subjected to electrical microstimulation have provided major evidence that the Central Nervous System (CNS) uses motor primitives, such as muscle synergies, to control motor tasks. Muscle synergies are thought to be a critical mechanism used by the CNS to control complex motor tasks by reducing the dimensional complexity of the system. Even though synergies have demonstrated potential for indicating how the body responds to balance perturbations by accounting for majority of the data set's variability, this concept has not been applied to slipping. To address this gap, data from 11 healthy young adults were collected and analyzed during both unperturbed walking and slipping. Applying an iterative non-negative matrix decomposition technique, four muscle synergies and the corresponding time-series activation coefficients were extracted. The synergies and the activation coefficients were then compared between baseline walking and slipping to determine shared vs. task-specific synergies. Correlation analyses found that among four synergies, two synergies were shared between normal walking and slipping. However, the other two synergies were task-specific. Both limbs were contributing to each of the four synergies, suggesting substantial inter-limb coordination during gait and slip. These findings stay consistent with previous unilateral studies that reported similar synergies between unperturbed and perturbed walking. Activation coefficients corresponding to the two shared synergies were similar between normal walking and slipping for the first 200 ms after heel contact and differed later in stance, suggesting the activation of muscle synergies in response to a slip. A muscle synergy approach would reveal the used sub-tasks during slipping, facilitating identification of impaired sub-tasks, and potentially leading to a purposeful rehabilitation based on damaged sub-functions

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait

Anàlisi de les sinergies musculars mitjançant electromiografia

Aquest Treball de Fi de Grau d’Enginyeria en Tecnologies Industrials descriu els passos per a realitzar l’anàlisi de les sinergies musculars a partir de les dades obtingudes mitjançant captures d’electromiografia. Es van realitzar les mesures del cicle de la marxa al Laboratori de Biomecànica de l’UPC i es van processar mitjançant el programari MATLAB R2013b. Les captures electromiogràfiques es van realitzar a un total de 12 subjectes que van participar en aquest projecte de manera voluntària. Aquest conjunt es divideix en 4 subjectes no lesionats i 8 subjectes amb ruptura fribil·lar al lligament creuat anterior d’una de les dues cames. La majora dels pacients van ser remesos pel Dr. Joan Carles Monllau, traumatòleg de la Clínica Dexeus i de l’Hospital del Mar de Barcelona. En aquesta memòria es presenten els instruments presents a laboratori per a realitzar les sessions d’electromiografia, així com la metodologia necessària per a la seva preparació i utilització i, finalment, la descripció del processament del senyal mioelèctric obtingut per a una posterior anàlisi de les sinergies musculars que intervenen en el moviment. En aquest projecte es presenten els resultats per al càlcul de les sinergies musculars, que són mòduls que defineixen patrons comuns per a la co-activació dels músculs durant el moviment. En aquests resultats s’observen diferències en l’estat d’activació dels músculs per als diferents conjunts de subjectes que s’estudien en el projecte, així com una variabilitat important en els resultats que indiquen la necessitat de dur a terme una anàlisi estadística per a descartar que les diferències provinguin d’un mal enregistrament de l’EMG, així com incloure més subjectes en l’estudi per a millorar els resultats estadístics de la mostra